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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> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=werkzeug&package-manager=uv&previous-version=3.1.1&new-version=3.1.6)](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 alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/semantic-kernel/network/alerts). </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@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: 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 aiohttp from 3.13.2 to 3.13.3 in /python (#13494) [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=aiohttp&package-manager=uv&previous-version=3.13.2&new-version=3.13.3)](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 alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/semantic-kernel/network/alerts). </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-02-10 09:50:04 +01:00
version = "3.13.3"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aiohappyeyeballs", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "aiosignal", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "async-timeout", 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')" },
{ name = "attrs", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "frozenlist", 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: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
{ name = "propcache", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "yarl", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2026-03-12 10:02:02 -07:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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. --> ### 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: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
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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"
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
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "azure-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
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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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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
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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: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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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: 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: 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: 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: 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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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: 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: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
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Python: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
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Python: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
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Python: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
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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: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
version = "11.7.0b2"
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Python: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
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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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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: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :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. <!-- 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: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +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 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: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +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
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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
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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: 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: 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: 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: 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:
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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:
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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
]
Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +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: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +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: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
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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: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +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
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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: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
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Python: Fix AzureAIAgent intermediate message ordering for invoke and invoke_stream (#12005) ### Motivation and Context This PR fixes a core issue in `AzureAIAgent` where `FunctionCallContent` and `FunctionResultContent` messages were not being emitted in the correct order during `invoke()` and `invoke_stream()` execution. - `invoke_stream`: tool messages are now drained and emitted after each streamed assistant chunk, preserving causal ordering (tool call -> result -> assistant reply). - `invoke`: tool messages are emitted via callback immediately after the non-streaming response is received, ensuring they precede the final assistant message if applicable. ### Description Updated samples to better show how the callbacks are used to receive the intermediate messages in the proper order. - Added unit tests to ensure proper coverage and message ordering. - Closes #11999 <!-- 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:
2025-05-13 07:58:06 +09:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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 support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +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>
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Python: Add OpenAI's sequence_number attribute to unit tests based on their latest package (#12227) ### Motivation and Context OpenAI release a new python package (1.81.0) which has some updates to the underlying Pydantic models for the Responses API. We need to pull in a new change for `ResponseOutputItemDoneEvent`, `ResponseOutputItemAddedEvent`, and `ResponseTextDeltaEvent` for streaming invocation which now has `sequence_number` attribute. <!-- 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 a change from OpenAI's Python SDK from v1.81.0 related to SK's OpenAIResponsesAgent. <!-- 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:
2025-05-22 13:20:29 +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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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: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Add OpenAI's sequence_number attribute to unit tests based on their latest package (#12227) ### Motivation and Context OpenAI release a new python package (1.81.0) which has some updates to the underlying Pydantic models for the Responses API. We need to pull in a new change for `ResponseOutputItemDoneEvent`, `ResponseOutputItemAddedEvent`, and `ResponseTextDeltaEvent` for streaming invocation which now has `sequence_number` attribute. <!-- 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 a change from OpenAI's Python SDK from v1.81.0 related to SK's OpenAIResponsesAgent. <!-- 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:
2025-05-22 13:20:29 +09:00
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name = "google-genai"
Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Add OpenAI's sequence_number attribute to unit tests based on their latest package (#12227) ### Motivation and Context OpenAI release a new python package (1.81.0) which has some updates to the underlying Pydantic models for the Responses API. We need to pull in a new change for `ResponseOutputItemDoneEvent`, `ResponseOutputItemAddedEvent`, and `ResponseTextDeltaEvent` for streaming invocation which now has `sequence_number` attribute. <!-- 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 a change from OpenAI's Python SDK from v1.81.0 related to SK's OpenAIResponsesAgent. <!-- 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:
2025-05-22 13:20:29 +09:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Add OpenAI's sequence_number attribute to unit tests based on their latest package (#12227) ### Motivation and Context OpenAI release a new python package (1.81.0) which has some updates to the underlying Pydantic models for the Responses API. We need to pull in a new change for `ResponseOutputItemDoneEvent`, `ResponseOutputItemAddedEvent`, and `ResponseTextDeltaEvent` for streaming invocation which now has `sequence_number` attribute. <!-- 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 a change from OpenAI's Python SDK from v1.81.0 related to SK's OpenAIResponsesAgent. <!-- 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:
2025-05-22 13:20:29 +09:00
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2026-03-12 10:02:02 -07:00
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Python: Add OpenAI's sequence_number attribute to unit tests based on their latest package (#12227) ### Motivation and Context OpenAI release a new python package (1.81.0) which has some updates to the underlying Pydantic models for the Responses API. We need to pull in a new change for `ResponseOutputItemDoneEvent`, `ResponseOutputItemAddedEvent`, and `ResponseTextDeltaEvent` for streaming invocation which now has `sequence_number` attribute. <!-- 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 a change from OpenAI's Python SDK from v1.81.0 related to SK's OpenAIResponsesAgent. <!-- 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:
2025-05-22 13:20:29 +09:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Fix AzureAIAgent intermediate message ordering for invoke and invoke_stream (#12005) ### Motivation and Context This PR fixes a core issue in `AzureAIAgent` where `FunctionCallContent` and `FunctionResultContent` messages were not being emitted in the correct order during `invoke()` and `invoke_stream()` execution. - `invoke_stream`: tool messages are now drained and emitted after each streamed assistant chunk, preserving causal ordering (tool call -> result -> assistant reply). - `invoke`: tool messages are emitted via callback immediately after the non-streaming response is received, ensuring they precede the final assistant message if applicable. ### Description Updated samples to better show how the callbacks are used to receive the intermediate messages in the proper order. - Added unit tests to ensure proper coverage and message ordering. - Closes #11999 <!-- 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:
2025-05-13 07:58:06 +09:00
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Python: Fix AzureAIAgent intermediate message ordering for invoke and invoke_stream (#12005) ### Motivation and Context This PR fixes a core issue in `AzureAIAgent` where `FunctionCallContent` and `FunctionResultContent` messages were not being emitted in the correct order during `invoke()` and `invoke_stream()` execution. - `invoke_stream`: tool messages are now drained and emitted after each streamed assistant chunk, preserving causal ordering (tool call -> result -> assistant reply). - `invoke`: tool messages are emitted via callback immediately after the non-streaming response is received, ensuring they precede the final assistant message if applicable. ### Description Updated samples to better show how the callbacks are used to receive the intermediate messages in the proper order. - Added unit tests to ensure proper coverage and message ordering. - Closes #11999 <!-- 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:
2025-05-13 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
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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:
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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! 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>
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Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +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
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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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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: Fix AzureAIAgent intermediate message ordering for invoke and invoke_stream (#12005) ### Motivation and Context This PR fixes a core issue in `AzureAIAgent` where `FunctionCallContent` and `FunctionResultContent` messages were not being emitted in the correct order during `invoke()` and `invoke_stream()` execution. - `invoke_stream`: tool messages are now drained and emitted after each streamed assistant chunk, preserving causal ordering (tool call -> result -> assistant reply). - `invoke`: tool messages are emitted via callback immediately after the non-streaming response is received, ensuring they precede the final assistant message if applicable. ### Description Updated samples to better show how the callbacks are used to receive the intermediate messages in the proper order. - Added unit tests to ensure proper coverage and message ordering. - Closes #11999 <!-- 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:
2025-05-13 07:58:06 +09:00
]
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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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
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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
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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: 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: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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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: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
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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? 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: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Introduce the Copilot Studio Agent (#11851) ### Motivation and Context Semantic Kernel Python currently has a demo showing how to interact with a Copilot Studio Agent; however, that uses the `DirectLine API`. This PR introduces a Semantic Kernel `CoilotStudioAgent` that leverages the team's new `microsoft.agents.copilotstudio.client`. One must first have a Copilot Studio Agent created via the portal; and once ready, they can reference it with the proper App Registration and Agent metadata. Interacting with the Agent can leverage Microsoft Copilot Studio's knowledge sources and internal tools. <!-- 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 the `CopilotStudioAgent`. - Provide getting started samples - Introduces a detailed README on what is required for setup and interacting with the agent. - The current version of the `CopilotStudioAgent` does not support calling another Copilot Studio Agent via a skill. - Authentication for the agent is limited to interactive right now. Once service-to-service auth is support via the client, we will add this into SK. - Closes #11607 <!-- 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:
2025-05-06 19:29:29 +09:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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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: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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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: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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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: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 18:01:51 -07:00
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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: 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: 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: 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: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
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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&amp;to=2026-01-29&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;to=2026-01-29&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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&amp;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> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=nbconvert&package-manager=uv&previous-version=7.16.6&new-version=7.17.0)](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 alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/semantic-kernel/network/alerts). </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-02-10 09:37:58 +01:00
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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. <!-- 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? 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 AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
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Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +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: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +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: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +09:00
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Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +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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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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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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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 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. --> 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: Add OpenAI's sequence_number attribute to unit tests based on their latest package (#12227) ### Motivation and Context OpenAI release a new python package (1.81.0) which has some updates to the underlying Pydantic models for the Responses API. We need to pull in a new change for `ResponseOutputItemDoneEvent`, `ResponseOutputItemAddedEvent`, and `ResponseTextDeltaEvent` for streaming invocation which now has `sequence_number` attribute. <!-- 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 a change from OpenAI's Python SDK from v1.81.0 related to SK's OpenAIResponsesAgent. <!-- 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:
2025-05-22 13:20:29 +09:00
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Python: Add OpenAI's sequence_number attribute to unit tests based on their latest package (#12227) ### Motivation and Context OpenAI release a new python package (1.81.0) which has some updates to the underlying Pydantic models for the Responses API. We need to pull in a new change for `ResponseOutputItemDoneEvent`, `ResponseOutputItemAddedEvent`, and `ResponseTextDeltaEvent` for streaming invocation which now has `sequence_number` attribute. <!-- 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 a change from OpenAI's Python SDK from v1.81.0 related to SK's OpenAIResponsesAgent. <!-- 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:
2025-05-22 13:20:29 +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 OpenAI's sequence_number attribute to unit tests based on their latest package (#12227) ### Motivation and Context OpenAI release a new python package (1.81.0) which has some updates to the underlying Pydantic models for the Responses API. We need to pull in a new change for `ResponseOutputItemDoneEvent`, `ResponseOutputItemAddedEvent`, and `ResponseTextDeltaEvent` for streaming invocation which now has `sequence_number` attribute. <!-- 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 a change from OpenAI's Python SDK from v1.81.0 related to SK's OpenAIResponsesAgent. <!-- 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:
2025-05-22 13:20:29 +09:00
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Python: Add OpenAI's sequence_number attribute to unit tests based on their latest package (#12227) ### Motivation and Context OpenAI release a new python package (1.81.0) which has some updates to the underlying Pydantic models for the Responses API. We need to pull in a new change for `ResponseOutputItemDoneEvent`, `ResponseOutputItemAddedEvent`, and `ResponseTextDeltaEvent` for streaming invocation which now has `sequence_number` attribute. <!-- 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 a change from OpenAI's Python SDK from v1.81.0 related to SK's OpenAIResponsesAgent. <!-- 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:
2025-05-22 13:20:29 +09:00
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Python: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
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Python: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
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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> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=werkzeug&package-manager=uv&previous-version=3.1.1&new-version=3.1.6)](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 alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/semantic-kernel/network/alerts). </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-03-19 12:20:39 +01:00
version = "0.19.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "isodate", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ 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 /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=werkzeug&package-manager=uv&previous-version=3.1.1&new-version=3.1.6)](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 alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/semantic-kernel/network/alerts). </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-03-19 12:20:39 +01:00
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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> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=werkzeug&package-manager=uv&previous-version=3.1.1&new-version=3.1.6)](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 alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/semantic-kernel/network/alerts). </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-03-19 12:20:39 +01:00
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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. <!-- 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: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
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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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2026-03-12 10:02:02 -07:00
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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: 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: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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. --> ### 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. <!-- 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: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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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: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +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 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: 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() &amp;&amp; !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>... 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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:
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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: 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: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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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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Python: Introduce the Copilot Studio Agent (#11851) ### Motivation and Context Semantic Kernel Python currently has a demo showing how to interact with a Copilot Studio Agent; however, that uses the `DirectLine API`. This PR introduces a Semantic Kernel `CoilotStudioAgent` that leverages the team's new `microsoft.agents.copilotstudio.client`. One must first have a Copilot Studio Agent created via the portal; and once ready, they can reference it with the proper App Registration and Agent metadata. Interacting with the Agent can leverage Microsoft Copilot Studio's knowledge sources and internal tools. <!-- 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 the `CopilotStudioAgent`. - Provide getting started samples - Introduces a detailed README on what is required for setup and interacting with the agent. - The current version of the `CopilotStudioAgent` does not support calling another Copilot Studio Agent via a skill. - Authentication for the agent is limited to interactive right now. Once service-to-service auth is support via the client, we will add this into SK. - Closes #11607 <!-- 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:
2025-05-06 19:29:29 +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: 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 AzureAIAgent intermediate message ordering for invoke and invoke_stream (#12005) ### Motivation and Context This PR fixes a core issue in `AzureAIAgent` where `FunctionCallContent` and `FunctionResultContent` messages were not being emitted in the correct order during `invoke()` and `invoke_stream()` execution. - `invoke_stream`: tool messages are now drained and emitted after each streamed assistant chunk, preserving causal ordering (tool call -> result -> assistant reply). - `invoke`: tool messages are emitted via callback immediately after the non-streaming response is received, ensuring they precede the final assistant message if applicable. ### Description Updated samples to better show how the callbacks are used to receive the intermediate messages in the proper order. - Added unit tests to ensure proper coverage and message ordering. - Closes #11999 <!-- 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:
2025-05-13 07:58:06 +09:00
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Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
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Python: Fix AzureAIAgent intermediate message ordering for invoke and invoke_stream (#12005) ### Motivation and Context This PR fixes a core issue in `AzureAIAgent` where `FunctionCallContent` and `FunctionResultContent` messages were not being emitted in the correct order during `invoke()` and `invoke_stream()` execution. - `invoke_stream`: tool messages are now drained and emitted after each streamed assistant chunk, preserving causal ordering (tool call -> result -> assistant reply). - `invoke`: tool messages are emitted via callback immediately after the non-streaming response is received, ensuring they precede the final assistant message if applicable. ### Description Updated samples to better show how the callbacks are used to receive the intermediate messages in the proper order. - Added unit tests to ensure proper coverage and message ordering. - Closes #11999 <!-- 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:
2025-05-13 07:58:06 +09:00
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Python: Fix AzureAIAgent intermediate message ordering for invoke and invoke_stream (#12005) ### Motivation and Context This PR fixes a core issue in `AzureAIAgent` where `FunctionCallContent` and `FunctionResultContent` messages were not being emitted in the correct order during `invoke()` and `invoke_stream()` execution. - `invoke_stream`: tool messages are now drained and emitted after each streamed assistant chunk, preserving causal ordering (tool call -> result -> assistant reply). - `invoke`: tool messages are emitted via callback immediately after the non-streaming response is received, ensuring they precede the final assistant message if applicable. ### Description Updated samples to better show how the callbacks are used to receive the intermediate messages in the proper order. - Added unit tests to ensure proper coverage and message ordering. - Closes #11999 <!-- 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:
2025-05-13 07:58:06 +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>
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Python: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +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? 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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2026-03-12 10:02:02 -07:00
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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: 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: Introduce the Copilot Studio Agent (#11851) ### Motivation and Context Semantic Kernel Python currently has a demo showing how to interact with a Copilot Studio Agent; however, that uses the `DirectLine API`. This PR introduces a Semantic Kernel `CoilotStudioAgent` that leverages the team's new `microsoft.agents.copilotstudio.client`. One must first have a Copilot Studio Agent created via the portal; and once ready, they can reference it with the proper App Registration and Agent metadata. Interacting with the Agent can leverage Microsoft Copilot Studio's knowledge sources and internal tools. <!-- 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 the `CopilotStudioAgent`. - Provide getting started samples - Introduces a detailed README on what is required for setup and interacting with the agent. - The current version of the `CopilotStudioAgent` does not support calling another Copilot Studio Agent via a skill. - Authentication for the agent is limited to interactive right now. Once service-to-service auth is support via the client, we will add this into SK. - Closes #11607 <!-- 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:
2025-05-06 19:29:29 +09:00
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Python: Bump python-multipart from 0.0.20 to 0.0.22 in /python (#13493) Bumps [python-multipart](https://github.com/Kludex/python-multipart) from 0.0.20 to 0.0.22. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/Kludex/python-multipart/releases">python-multipart's releases</a>.</em></p> <blockquote> <h2>Version 0.0.22</h2> <h2>What's Changed</h2> <ul> <li>Drop directory path from filename in <code>File</code> <a href="https://github.com/Kludex/python-multipart/commit/9433f4bbc9652bdde82bbe380984e32f8cfc89c4">9433f4b</a>.</li> </ul> <hr /> <p><strong>Full Changelog</strong>: <a href="https://github.com/Kludex/python-multipart/compare/0.0.21...0.0.22">https://github.com/Kludex/python-multipart/compare/0.0.21...0.0.22</a></p> <h2>Version 0.0.21</h2> <h2>What's Changed</h2> <ul> <li>Add support for Python 3.14 and drop EOL 3.8 and 3.9 by <a href="https://github.com/hugovk"><code>@​hugovk</code></a> in <a href="https://redirect.github.com/Kludex/python-multipart/pull/216">Kludex/python-multipart#216</a></li> </ul> <h2>New Contributors</h2> <ul> <li><a href="https://github.com/waketzheng"><code>@​waketzheng</code></a> made their first contribution in <a href="https://redirect.github.com/Kludex/python-multipart/pull/203">Kludex/python-multipart#203</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/Kludex/python-multipart/compare/0.0.20...0.0.21">https://github.com/Kludex/python-multipart/compare/0.0.20...0.0.21</a></p> </blockquote> </details> <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/Kludex/python-multipart/blob/master/CHANGELOG.md">python-multipart's changelog</a>.</em></p> <blockquote> <h2>0.0.22 (2026-01-25)</h2> <ul> <li>Drop directory path from filename in <code>File</code> <a href="https://github.com/Kludex/python-multipart/commit/9433f4bbc9652bdde82bbe380984e32f8cfc89c4">9433f4b</a>.</li> </ul> <h2>0.0.21 (2025-12-17)</h2> <ul> <li>Add support for Python 3.14 and drop EOL 3.8 and 3.9 <a href="https://redirect.github.com/Kludex/python-multipart/pull/216">#216</a>.</li> </ul> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/Kludex/python-multipart/commit/bea7bbb2904da8ce39123a845088dc72464eaddf"><code>bea7bbb</code></a> Version 0.0.22 (<a href="https://redirect.github.com/Kludex/python-multipart/issues/222">#222</a>)</li> <li><a href="https://github.com/Kludex/python-multipart/commit/0fb59a9df0f273bfde99740b302ccb2ae45e2b8a"><code>0fb59a9</code></a> chore: add return type on test (<a href="https://redirect.github.com/Kludex/python-multipart/issues/221">#221</a>)</li> <li><a href="https://github.com/Kludex/python-multipart/commit/9433f4bbc9652bdde82bbe380984e32f8cfc89c4"><code>9433f4b</code></a> Merge commit from fork</li> <li><a href="https://github.com/Kludex/python-multipart/commit/d5c91ecb0aa1cae03fe2d9811d193c952e714f17"><code>d5c91ec</code></a> Bump the github-actions group with 2 updates (<a href="https://redirect.github.com/Kludex/python-multipart/issues/219">#219</a>)</li> <li><a href="https://github.com/Kludex/python-multipart/commit/5a90631b484f8d3284b691d453c24be3db57f5cb"><code>5a90631</code></a> bump uv (<a href="https://redirect.github.com/Kludex/python-multipart/issues/218">#218</a>)</li> <li><a href="https://github.com/Kludex/python-multipart/commit/1f72955602445706b5517a6f58a720796ad3d96a"><code>1f72955</code></a> Version 0.0.21 (<a href="https://redirect.github.com/Kludex/python-multipart/issues/217">#217</a>)</li> <li><a href="https://github.com/Kludex/python-multipart/commit/47ecfed3533ed8dcafd800508dbf594438fd0949"><code>47ecfed</code></a> Add support for Python 3.14 and drop EOL 3.8 and 3.9 (<a href="https://redirect.github.com/Kludex/python-multipart/issues/216">#216</a>)</li> <li><a href="https://github.com/Kludex/python-multipart/commit/f18b70941b727c947f7e6b17e1c3321f5ad3afb6"><code>f18b709</code></a> Bump the github-actions group across 1 directory with 4 updates (<a href="https://redirect.github.com/Kludex/python-multipart/issues/214">#214</a>)</li> <li><a href="https://github.com/Kludex/python-multipart/commit/b388e9a7a82605cc8613798926afe8f6074cb372"><code>b388e9a</code></a> chore: use depedency-groups in <code>pyproject.toml</code> (<a href="https://redirect.github.com/Kludex/python-multipart/issues/212">#212</a>)</li> <li><a href="https://github.com/Kludex/python-multipart/commit/6113e750971918a51f79c3bb2585e95ed1c53245"><code>6113e75</code></a> Bump the github-actions group across 1 directory with 3 updates (<a href="https://redirect.github.com/Kludex/python-multipart/issues/210">#210</a>)</li> <li>Additional commits viewable in <a href="https://github.com/Kludex/python-multipart/compare/0.0.20...0.0.22">compare view</a></li> </ul> </details> <br /> 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2026-02-10 09:50:36 +01:00
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Python: Introduce the Copilot Studio Agent (#11851) ### Motivation and Context Semantic Kernel Python currently has a demo showing how to interact with a Copilot Studio Agent; however, that uses the `DirectLine API`. This PR introduces a Semantic Kernel `CoilotStudioAgent` that leverages the team's new `microsoft.agents.copilotstudio.client`. One must first have a Copilot Studio Agent created via the portal; and once ready, they can reference it with the proper App Registration and Agent metadata. Interacting with the Agent can leverage Microsoft Copilot Studio's knowledge sources and internal tools. <!-- 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 the `CopilotStudioAgent`. - Provide getting started samples - Introduces a detailed README on what is required for setup and interacting with the agent. - The current version of the `CopilotStudioAgent` does not support calling another Copilot Studio Agent via a skill. - Authentication for the agent is limited to interactive right now. Once service-to-service auth is support via the client, we will add this into SK. - Closes #11607 <!-- 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:
2025-05-06 19:29:29 +09:00
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Python: Introduce the Copilot Studio Agent (#11851) ### Motivation and Context Semantic Kernel Python currently has a demo showing how to interact with a Copilot Studio Agent; however, that uses the `DirectLine API`. This PR introduces a Semantic Kernel `CoilotStudioAgent` that leverages the team's new `microsoft.agents.copilotstudio.client`. One must first have a Copilot Studio Agent created via the portal; and once ready, they can reference it with the proper App Registration and Agent metadata. Interacting with the Agent can leverage Microsoft Copilot Studio's knowledge sources and internal tools. <!-- 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 the `CopilotStudioAgent`. - Provide getting started samples - Introduces a detailed README on what is required for setup and interacting with the agent. - The current version of the `CopilotStudioAgent` does not support calling another Copilot Studio Agent via a skill. - Authentication for the agent is limited to interactive right now. Once service-to-service auth is support via the client, we will add this into SK. - Closes #11607 <!-- 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:
2025-05-06 19:29:29 +09:00
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Python: Introduce the Copilot Studio Agent (#11851) ### Motivation and Context Semantic Kernel Python currently has a demo showing how to interact with a Copilot Studio Agent; however, that uses the `DirectLine API`. This PR introduces a Semantic Kernel `CoilotStudioAgent` that leverages the team's new `microsoft.agents.copilotstudio.client`. One must first have a Copilot Studio Agent created via the portal; and once ready, they can reference it with the proper App Registration and Agent metadata. Interacting with the Agent can leverage Microsoft Copilot Studio's knowledge sources and internal tools. <!-- 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 the `CopilotStudioAgent`. - Provide getting started samples - Introduces a detailed README on what is required for setup and interacting with the agent. - The current version of the `CopilotStudioAgent` does not support calling another Copilot Studio Agent via a skill. - Authentication for the agent is limited to interactive right now. Once service-to-service auth is support via the client, we will add this into SK. - Closes #11607 <!-- 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: 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>
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +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 support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +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:
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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: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
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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. <!-- 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: 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 = "scipy"
Python: Fix AzureAIAgent intermediate message ordering for invoke and invoke_stream (#12005) ### Motivation and Context This PR fixes a core issue in `AzureAIAgent` where `FunctionCallContent` and `FunctionResultContent` messages were not being emitted in the correct order during `invoke()` and `invoke_stream()` execution. - `invoke_stream`: tool messages are now drained and emitted after each streamed assistant chunk, preserving causal ordering (tool call -> result -> assistant reply). - `invoke`: tool messages are emitted via callback immediately after the non-streaming response is received, ensuring they precede the final assistant message if applicable. ### Description Updated samples to better show how the callbacks are used to receive the intermediate messages in the proper order. - Added unit tests to ensure proper coverage and message ordering. - Closes #11999 <!-- 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:
2025-05-13 07:58:06 +09:00
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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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[[package]]
name = "semantic-kernel"
source = { editable = "." }
dependencies = [
{ name = "aiohttp", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ 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? 4. If it fixes an open issue, please link to the issue here. --> ### 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'" },
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Python: Add support for streaming OpenAI assistants (#9055) ### Motivation and Context OpenAI assistants were introduced several versions ago; however, there was open work to support streaming for OpenAI assistants v2. Work to support streaming assistants was also needed in Agent Group Chat scenarios. <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> ### Description This PR introduces: - Support for streaming with (Azure) OpenAI assistants - Support for Agent Chat Scenarios that can now leverage a streaming invoke instead of just a sync invoke. - Unit tests to increase the test coverage to near 100% for the newly added code - Adds a few more OpenAI assistant agent samples - Closes #7267 <!-- 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:
2024-10-02 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. <!-- 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 = "cloudevents", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ 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'" },
{ name = "mcp", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ 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'" },
{ 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'" },
{ 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`. <!-- 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
{ 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')" },
{ name = "typing-extensions", 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'" },
]
[package.optional-dependencies]
anthropic = [
{ name = "anthropic", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
autogen = [
{ name = "autogen-agentchat", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
Python: Bedrock connector (#9100) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Add Amazon Bedrock AI to Semantic Kernel. Amazon Bedrock is a platform on AWS that offers access to a wide range to LLMs, including proprietary Amazon Titan models. We are integrating it into Semantic Kernel as one of our AI connectors to provide more options to developers. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. All interfaces implemented: chat completion (streaming + non-streaming), text completion (streaming + non-streaming), and text embedding. 2. Supports for function calling. 3. Supports for image content. 4. Supports for all models available on Bedrock. ### 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:
2024-10-10 15:33:42 -07:00
aws = [
{ name = "boto3", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
azure = [
{ name = "azure-ai-inference", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
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'" },
{ 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'" },
]
Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 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'" },
]
faiss = [
{ name = "faiss-cpu", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
google = [
{ name = "google-cloud-aiplatform", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
{ name = "google-genai", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
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'" },
]
mcp = [
{ name = "mcp", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
milvus = [
Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +09:00
{ name = "milvus", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
{ name = "pymilvus", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
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'" },
Python: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
{ name = "pymongo", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
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'" },
]
Python: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
onnx = [
{ name = "onnxruntime", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "onnxruntime-genai", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: #6761 Onnx Connector (#8106) ### 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. --> 1. Why is this changed required ? To enable Onnx Models with Semantic Kernel, there was the issue #6761 in the Backlog to add a Onnx Connector 2. What problem does it solve ? It solves the problem, that semantic kernel is not yet integrated with Onnx Gen Ai Runtime 3. What scenario does it contribute to? The scenario is to use different connector than HF,OpenAI or AzureOpenAI. When User's want to use Onnx they can easliy integrate it now 4. If it fixes an open issue, please link to the issue here. #6761 ### Description The changes made are designed by my own based on other connectors, i tried to stay as close as possible to the structure. For the integration i installed the mistral python package in the repository. I added the following Classes : - OnnxCompletionBase --> Responsible to control the inference - OnnxTextCompletion --> Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) - OnnxChatCompletion -->Inherits from OnnxCompletionBase - Support for Text Completion with and without Images - The user needs to provide the corresponding chat template to use this class - Ready for Multimodal Inference - Ready for Text Only Inference - Supports all Models on [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) What is integrated yet : - [X] OnnxCompletionBase Class - [x] OnnxChatCompletionBase Class with Dynamic Template Support - [x] OnnxTextCompletionBase Class - [x] Sample Multimodal Inference with Phi3-Vision - [x] Sample of OnnxChatCompletions with Phi3 - [x] Sample of OnnxTextCompletions with Phi3 - [x] Integration Tests - [x] Unit Tests ### Some Notes ### 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: Tao Chen <taochen@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-10 19:41:21 +02:00
]
oracledb = [
{ name = "oracledb", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
pandas = [
{ name = "pandas", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
pinecone = [
{ name = "pinecone", extra = ["asyncio", "grpc"], marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
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'" },
]
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. <!-- 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
{ name = "qdrant-client", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
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'" },
]
redis = [
{ name = "redis", extra = ["hiredis"], marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "redisvl", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "types-redis", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
sql = [
{ name = "pyodbc", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
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'" },
{ name = "pytest-timeout", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ 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'" },
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[package.metadata]
requires-dist = [
{ name = "aiohttp", specifier = "~=3.8" },
{ name = "aiortc", specifier = ">=1.9.0" },
{ name = "aiortc", marker = "extra == 'realtime'", specifier = ">=1.9.0" },
{ name = "anthropic", marker = "extra == 'anthropic'", specifier = "~=0.32" },
Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
{ name = "autogen-agentchat", marker = "extra == 'autogen'", specifier = ">=0.2,<0.4" },
Python: Add AzureAIAgent Deep Research Tool support (#13034) ### Motivation and Context The deep research tool was introduced for the AzureAIAgent; however, we didn't have support yet in SK. Adding support now for using the deep research tool for both streaming and non-streaming. Samples added as well. <!-- 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 deep research tool. - Add samples with notes on prerequisites to configure the sample to run properly. - Added support for `AZURE_AI_AGENT_DEEP_RESEARCH_MODEL` env var within `AzureAIAgentSettings` class. - Bumped min allowed `azure-ai-agents` package ver to 1.2.0b3. - Closes #12978 <!-- 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:
2025-09-01 11:35:56 +09:00
{ name = "azure-ai-agents", specifier = ">=1.2.0b3" },
Python: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
{ name = "azure-ai-inference", marker = "extra == 'azure'", specifier = ">=1.0.0b6" },
Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
{ name = "azure-ai-projects", specifier = "~=1.0.0b12" },
Python: Azure AI Inference tracing SDK (#9693) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-11-19 11:25:25 -08:00
{ name = "azure-core-tracing-opentelemetry", marker = "extra == 'azure'", specifier = ">=1.0.0b11" },
{ 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. <!-- 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 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. <!-- 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:
2025-03-28 00:59:46 +09:00
{ name = "azure-identity", specifier = ">=1.13" },
{ 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 <!-- 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
{ 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. <!-- 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 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. <!-- 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:
2025-04-10 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. <!-- 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 = "cloudevents", specifier = "~=1.0" },
{ name = "defusedxml", specifier = "~=0.7" },
{ name = "faiss-cpu", marker = "extra == 'faiss'", specifier = ">=1.10.0" },
{ name = "google-cloud-aiplatform", marker = "extra == 'google'", specifier = "~=1.114.0" },
Python: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -08:00
{ name = "google-genai", marker = "extra == 'google'", specifier = "~=1.51.0" },
{ name = "ipykernel", marker = "extra == 'notebooks'", specifier = "~=6.29" },
{ name = "jinja2", specifier = "~=3.1" },
{ name = "mcp", specifier = ">=1.26.0" },
{ name = "mcp", marker = "extra == 'mcp'", specifier = ">=1.8" },
Python: [Python] Updated Agents SDK version to latest for CopilotStudio connector (#13144) ### 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? - The Agents SDK has been released, and the recent release changed the namespace from `microsoft.agents` to `microsoft_agents`, which breaks imports. 2. What problem does it solve? - This updates the `pyproject.toml` and copilot studio related files to fix the imports 3. What scenario does it contribute to? - Using a Copilot Studio agent as an Agent in Semantic Kernel 4. If it fixes an open issue, please link to the issue here. - N/A --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Updated pyproject.toml to use the released packages for the Agents SDK. - Updated imports in the `copilot_studio_agent` related files to use the new `microsoft_agents` namespace ### 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:
2025-09-22 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" },
Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +09:00
{ name = "milvus", marker = "sys_platform != 'win32' and extra == 'milvus'", specifier = ">=2.3,<2.3.8" },
{ name = "mistralai", marker = "extra == 'mistralai'", specifier = ">=1.2,<2.0" },
Python: add AzureAIAgent client creation method. Handle user_agent. (#10538) ### Motivation and Context We weren't sending the user_agent as part of client creation for the AzureAIAgent. Create a convenience class method to create the AIProjectClient, which handles creating the user_agent. <!-- 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: - adds a class method to create the AIProjectClient as part of AzureAIAgent. - handles configuring the user_agent. - fixes a check where annotation content indices might not be present - adds `/.default` to default entra scope - adds unit tests - closes #10527 - closes #10520 <!-- 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:
2025-02-14 14:20:24 +09:00
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2024-09-16 14:13:23 -04:00
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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 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. --> 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 (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: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +02:00
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Python: Fix ResponsesAgent image duplication in chat history preparation (#12859) ### Motivation and Context The ResponsesAgent is duplicating messages in chat history preparation when messages contained multiple content items (text + images). The `response_inputs.append()` call is inside the content processing loop, causing each message to be added once per content item. The fix: - Moved `response_inputs.append()` outside the content processing loop - Added unit test to verify multiple images don't get duplicated <!-- 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 - Fixed loop placement in `_prepare_chat_history_for_request` - Added test `test_prepare_chat_history_multiple_images_no_duplication` - Fixes #12848 <!-- 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:
2025-08-05 23:58:35 +09:00
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Python: Introduce the OpenAI Responses Agent (#11240) ### Motivation and Context OpenAI recently released their Responses API, which is their newest core API and an agentic API primitive, combining the simplicity of Chat Completions with the ability to do more agentic tasks. Azure OpenAI also recently released the Responses API, with close-to feature parity with OpenAI's SDK. It gives us enough to introduce an `AzureResponsesAgent` and an `OpenAIResponsesAgent` to Semantic Kernel. <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> ### Description This PR introduces: - The `AzureResponsesAgent` and `OpenAIResponsesAgent` - Allows use of the web search tool with `OpenAI` (`Azure OpenAI` currently doesn't support this). - Allows use of the file search tool with both `OpenAI` and `Azure OpenAI`. - Provides getting started samples for both agent types. **TODO:** - Add unit test coverage for the `ResponsesAgentThreadActions` class. - Add support for the computer user agent tool. - Improve typing in the `ResponsesAgentThreadActions` class. - Closes #11026 <!-- 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:
2025-04-03 08:18:57 +09:00
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Python: Feature python vector stores preb (#12271) ### 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. --> Overhaul of the VectorStores to get it matching with dotnet and the latest development there. Closes: - #11938 - #11598 - #11597 - #11517 - #10561 - #10391 - #9911 - #9892 - #10867 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Many changes, some highlights: - Moved from different vector fields types, to a single with a enum first VectorStoreField() - Renamed several items - Made embeddinggenerrator a part of the vector field spec and do automatic vectorization from there or from the same param on a collection - Adds hybrid search using the same setup - Removed the intermediate TextSeachVectorSearch object need, by allowing `create_search_function` directly on a collection Will write out full changes in a migration guide in the docs. ### 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:
2025-06-19 20:34:59 +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 <!-- 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 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 ``` <!-- 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:
2025-07-23 13:07:20 +09:00
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2026-03-12 10:02:02 -07:00
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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: 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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2026-03-12 10:02:02 -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 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? 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: 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: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +09:00
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Python: Add support for AutoGen's 0.2 ConversableAgent (#10607) ### Motivation and Context For those who use AutoGen's 0.2 `ConversableAgent`, we're providing support in Semantic Kernel to run this agent type. This assumes one will port their existing AG 0.2.X `ConversableAgent` code and run in Semantic Kernel. Note: as we move towards GA for our agent chat patterns, we are analyzing how to support AutoGen agents with a shared time. This PR does not provide support for the AG `ConversibleAgent` group chat patterns that exist in the 0.2.X package. <!-- 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 and samples for the AG 0.2.X ConversableAgent. - Add unit test coverage. - Add samples and READMEs. - Closes #10407 <!-- 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: Chris <66376200+crickman@users.noreply.github.com>
2025-02-20 20:11:05 +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: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +09:00
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Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +09:00
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2024-11-15 09:43:15 -05:00
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Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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:
2025-03-26 11:29:12 +09:00
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Python: Allow for Agents as Kernel Functions (#11184) ### Motivation and Context We're moving towards a pattern where Agents can be called as tools. We currently don't have this in Semantic Kernel Python. This work builds off of the previous PR (#10828) but extends it to work with any SK Agent now that we have a bit more of a common invocation pattern (all agents take in `messages` kwarg). This also works now because each agent creates its own thread internally, if one isn't provided - so we don't need to worry about handling chat history for an OpenAI Assistant or an Azure AI Agent. <!-- 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 Allow agents to be configured as Kernel Functions, which can then be used during Function Calling. We will advertise the Agent's name and either its description or instructions (if description is blank). - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (ChatCompletionAgent) or a refund agent (ChatCompletionAgent ) as part of a tool call to satisfy the user's query. - Provide a sample where we show a triage agent (ChatCompletionAgent) calling either a billing agent (AzureAIAgent) or a refund agent (ChatCompletionAgent) to satisfy the user's query. <!-- 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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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: 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: Migrate to new Google GenAI SDK (#13371) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Closes #12970, #13352 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Migrate Google AI connector to use the new Google AI SDK. 2. This PR also marks the VertexAI connector as deprecated in favor of the Google AI connector. With this update, if users wants to connect to a VertexAI project instead of a Google AI endpoint, they can do so with the Google AI connector. 3. This PR also adds the ability for users to provide a custom client to the Google AI connector (#13352). 4. This PR also marks `AzureTextCompletion` deprecated. ### 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:
2025-11-24 13:53:57 -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 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: 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: 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:
2026-02-28 09:01:03 +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:
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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> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=werkzeug&package-manager=uv&previous-version=3.1.1&new-version=3.1.6)](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 alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. 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2026-03-19 12:20:39 +01:00
version = "3.1.6"
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 /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=werkzeug&package-manager=uv&previous-version=3.1.1&new-version=3.1.6)](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 alter it yourself. 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[//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/semantic-kernel/network/alerts). </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-03-19 12:20:39 +01:00
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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> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=werkzeug&package-manager=uv&previous-version=3.1.1&new-version=3.1.6)](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 alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/semantic-kernel/network/alerts). </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-03-19 12:20:39 +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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