### Motivation and Context
Bump Pythong version to 1.41.1 for a release
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### Description
Bump Pythong version to 1.41.1 for a release
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### Contribution Checklist
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- [X] The code builds clean without any errors or warnings
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raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone 😄
### Motivation and Context
Closes#12442
When `ChatCompletionAgent` instances are used as plugins with
`GoogleAIChatCompletion`, the application crashes with:
```
ValueError: Unknown field for Schema: anyOf
```
**Root cause**: The Semantic Kernel JSON schema builder generates
`anyOf` for Union types (e.g. `str | list[str]` on the agent's
`messages` parameter) and type-as-array for Optional types (e.g.
`["string", "null"]`). Google AI's protobuf `Schema` does not support
these constructs.
### Description
Add `sanitize_schema_for_google_ai()` to `shared_utils.py` that
recursively rewrites unsupported schema constructs:
- `anyOf` / `oneOf` with a null variant → single type + `nullable: true`
- `anyOf` / `oneOf` without null → picks the first variant
- `type` as array (e.g. `["string", "null"]`) → single type + `nullable:
true`
Applied in both
`kernel_function_metadata_to_google_ai_function_call_format()` and
`kernel_function_metadata_to_vertex_ai_function_call_format()`.
### Contribution Checklist
- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [x] The existing tests pass, and I have added 12 new regression tests
- [x] I didn't break any existing functionality
> [!NOTE]
> AI-assisted contribution: Claude helped with code review, test
generation, and formatting.
---------
Co-authored-by: Claude Opus 4.6 <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
## Summary
Fixes#13483
### Motivation and Context
When `use_vertexai=True`, users authenticate via Application Default
Credentials (ADC) and don't need an API key. However, the initialization
check for `api_key` runs unconditionally, blocking Vertex AI users with:
```
ServiceInitializationError: The API key is required when use_vertexai is False.
```
### Description
Guard the `api_key` validation with `not
google_ai_settings.use_vertexai` in all three Google AI service classes:
- `GoogleAIChatCompletion`
- `GoogleAITextCompletion`
- `GoogleAITextEmbedding`
The error message already reads *"when use_vertexai is False"*, so the
condition now matches the intent.
### Before / After
```python
# Before — fails even with use_vertexai=True
GoogleAIChatCompletion(
use_vertexai=True,
cloud_project_id="my-project",
cloud_region="us-central1"
)
# → ServiceInitializationError: The API key is required when use_vertexai is False.
# After — works correctly
GoogleAIChatCompletion(
use_vertexai=True,
cloud_project_id="my-project",
cloud_region="us-central1"
)
# → Initializes successfully, uses ADC for authentication
```
### Contribution Checklist
- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [x] The code follows the [SK coding
standards](https://github.com/microsoft/semantic-kernel/blob/main/python/README.md)
---------
Co-authored-by: giulio-leone <[email protected]>
Co-authored-by: giulio-leone <[email protected]>
Co-authored-by: Copilot <[email protected]>
### Motivation and Context
Follow-up to #13499. The previous PR added the `allowed_module_prefixes`
parameter but defaulted it to `None`, which meant the module restriction
was only active if developers discovered and configured it.
Secure-by-default is the right posture here — restrict first, let
developers widen as needed.
- Change `allowed_module_prefixes` default from `None` to
`("semantic_kernel.",)` across Dapr runtime step loading
- Non-SK step classes now require developers to explicitly add their
module prefix (e.g. `("semantic_kernel.", "myapp.steps.")`)
- Developers can pass `None` to opt out entirely, but the secure default
is now enforced
- The Dapr runtime code is experimental, so this is a non-breaking
change per our stability guarantees
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### Contribution Checklist
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- [X] The code builds clean without any errors or warnings
- [X] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
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raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
---------
Co-authored-by: MAF Dashboard Bot <[email protected]>
Co-authored-by: Copilot <[email protected]>
## Summary
Fixes#12708
`ChatHistoryTruncationReducer.reduce()` can orphan `TOOL` role messages
by truncating the preceding `assistant` message that contains the
`tool_calls`, causing OpenAI to reject the history with:
```
messages with role 'tool' must be a response to a preceding message with 'tool_calls'
```
## Root Cause
`locate_safe_reduction_index()` uses
`contains_function_call_or_result()` to detect tool-related messages
during its backward scan. This function only checks `msg.items` for
`FunctionCallContent`/`FunctionResultContent` instances.
However, `TOOL` role messages can contain only text content (no
`FunctionResultContent` in `items`), which causes the backward scan to
treat them as regular messages. The truncation point then lands between
the `tool_calls` assistant message and its `TOOL` responses, orphaning
the tool results.
## Fix
Added `AuthorRole.TOOL` check to `contains_function_call_or_result()`:
```python
if msg.role == AuthorRole.TOOL:
return True
```
This ensures **any** `TOOL` role message is recognized as part of a tool
call/result pair, regardless of whether it has `FunctionResultContent`
in its `items`.
## Test
Added
`test_locate_safe_reduction_index_tool_role_without_function_result_content`
that verifies TOOL role messages with only text content are not
separated from their tool call.
---------
Co-authored-by: giulio-leone <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Copilot <[email protected]>
## Summary
`ChatHistoryTruncationReducer.reduce()` silently deletes
system/developer messages when truncating chat history. This is because
it calls `extract_range()`, which unconditionally filters out
system/developer messages — a function designed for the summarization
use case, not truncation.
Fixes#12612.
## Approach
Port the .NET SDK fix (PR #10344) to Python:
1. Detect the first system/developer message before truncation
2. Pass `has_system_message=True` to `locate_safe_reduction_index` so
`target_count` accounts for the preserved message (matches .NET's
`targetCount -= hasSystemMessage ? 1 : 0`)
3. Use a simple `history[truncation_index:]` slice instead of
`extract_range` (which strips system messages)
4. Prepend the system message if it was truncated away
Also adds a guard for `target_count <= 0` after the system message
adjustment to prevent `IndexError` when `target_count=1`.
## Changes
| File | Change |
|------|--------|
| `chat_history_reducer_utils.py` | Add `has_system_message` parameter,
adjust `target_count`, guard against `<= 0` |
| `chat_history_truncation_reducer.py` | Detect system message, bypass
`extract_range`, prepend if truncated |
| `test_chat_history_truncation_reducer.py` | Update 2 existing tests +
4 new tests (system, developer, no-system, target_count=1) |
## Note
The summarization reducer (`ChatHistorySummarizationReducer`) has the
same bug — it also uses `extract_range` and loses system messages during
summarization. The .NET summarization reducer preserves system messages
via `AssemblySummarizedHistory`. This should be addressed in a follow-up
PR to keep this change focused.
## Test plan
- [x] 11/11 truncation reducer tests pass (was 7, now 11)
- [x] 9/9 utils tests pass (no regressions from new parameter)
- [x] 11/11 summarization reducer tests pass (unaffected)
- [x] Reproduces and fixes the exact scenario from #12612
- [x] Edge case: `target_count=1` with system message does not crash
---------
Co-authored-by: Claude Opus 4.6 <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
Fixes#13632
When a prompt contains HTML tags such as `<p>`, `<div>`, `<b>`, etc.,
the
`ChatHistory.from_rendered_prompt()` method wraps the rendered prompt in
a
`<root>` element and parses it as XML. Any child elements whose tag
names do
not match the known SK template tags (`message`, `chat_history`) were
silently
skipped, causing their text content to be lost. This meant a prompt
like:
```
Translate: "<p>What is your name?</p>"
```
would arrive at the LLM as an empty message, because `<p>What is your
name?</p>`
was discarded during XML traversal.
### Description
In `ChatHistory.from_rendered_prompt`, when an XML child element has an
unrecognized tag (i.e. it is not `message` or `chat_history`), the
element
is now serialized back to its original text representation and appended
to
the preceding message content. The element's tail text is likewise kept
in
the same message, preserving the full original prompt text.
**Changed files:**
- `python/semantic_kernel/contents/chat_history.py` -- handle unknown
XML
tags as literal text in `from_rendered_prompt()`
- `python/tests/unit/contents/test_chat_history.py` -- added four
regression
tests covering HTML `<p>`, multiple HTML tags, HTML surrounded by text,
and SK tags mixed with HTML
### Contribution Checklist
- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [x] The existing tests pass and new tests have been added
- [x] I didn't break any existing functionality
---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
Fixes#13480
When using Gemini models (2.5 Pro / 3 Pro) with thinking enabled, the
API returns a `thought_signature` on function call `Part` objects. Per
[Google's Thought Signatures
documentation](https://ai.google.dev/gemini-api/docs/thought-signatures),
this signature **must** be included in subsequent requests. Without it,
follow-up turns with tool results fail with:
```
HTTP 400: Function call is missing a thought_signature in functionCall parts
```
The .NET connector was already fixed in #13418. This PR applies the
equivalent fix to the Python connector.
### Description
**Response parsing** (capture `thought_signature`):
- `google_ai_chat_completion.py`: When creating `FunctionCallContent`
from response parts, capture `part.thought_signature` into
`FunctionCallContent.metadata` (both non-streaming and streaming paths)
- `vertex_ai_chat_completion.py`: Same capture via
`part_dict.get("thought_signature")` for Vertex AI
**Request building** (restore `thought_signature`):
- `google_ai/services/utils.py`: In `format_assistant_message()`, when a
`FunctionCallContent` has `thought_signature` in its metadata, construct
the `Part` directly with the `thought_signature` parameter instead of
using `Part.from_function_call()`
- `vertex_ai/services/utils.py`: In `format_assistant_message()`,
include `thought_signature` in the `Part.from_dict()` dictionary when
present
**Backward compatible**: When `thought_signature` is not present
(thinking disabled or older models), behavior is identical to before.
### Test Coverage
Added 4 new tests:
- `test_format_assistant_message_with_thought_signature` (Google AI) —
verifies round-trip preservation
- `test_format_assistant_message_without_thought_signature` (Google AI)
— verifies backward compatibility
- `test_format_assistant_message_with_thought_signature` (Vertex AI) —
verifies round-trip preservation
- `test_format_assistant_message_without_thought_signature` (Vertex AI)
— verifies backward compatibility
All 110 existing Google connector tests continue to pass.
### Contribution Checklist
- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [x] All unit tests pass, and I have added new tests
- [x] I didn't break anyone 😄
---------
Co-authored-by: giulio-leone <[email protected]>
Co-authored-by: Copilot <[email protected]>
### Motivation and Context
Bump Python version to 1.41.0 for a release
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### Description
Bump Python version to 1.41.0 for a release
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- [ ] I didn't break anyone 😄
### Motivation and Context
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Dall-e-3 and Dall-e-2 have been deprecated. The newer gpt-1-image model
returns base64 strings.
### Description
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Return url if set, otherwise return the base64 string.
### Contribution Checklist
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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
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Fixes another way of escaping allowed commands.
### Description
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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
### Motivation and Context
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Improves the robustness of filename handling in the Bedrock agent
integration. Ensures that filenames sourced from external responses are
properly sanitized before use in file operations, and adds safer
defaults for file write behavior.
### Description
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1. Sanitize filenames received from Bedrock agent responses.
2. Add overwrite parameter to BinaryContent.write_to_file (defaults to
False).
3. Add tests.
4. Update samples.
### Contribution Checklist
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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
### Motivation and Context
Bump py version to 1.40.0 for a release
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### Description
Bump py version to 1.40.0 for a release
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raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
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### Description
- Closes#13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
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raises no violations
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- [ ] I didn't break anyone 😄
### Motivation and Context
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Bumps version
Fixes new MCP version package, and upgrades some other packages.
### Description
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raises no violations
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- [ ] I didn't break anyone 😄
### Motivation and Context
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further refinement of in memory filtering
### Description
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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
### Motivation and Context
Bump Python version to 1.39.3 for a release
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### Description
Bump Python version to 1.39.3 for a release
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raises no violations
- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
### Motivation and Context
The Dapr Runtime uses string-based class names to load step classes
dynamically. This PR adds validation to ensure that only valid
KernelProcessStep subclasses can be loaded and instantiated, improving
type safety and providing better error messages when misconfigured.
The new allowed_module_prefixes parameter gives users control over which
modules are permitted for step class loading, which can be useful in
environments where stricter control is desired.
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### Description
- Add issubclass(cls, KernelProcessStep) validation when loading step
classes from qualified names
- Add optional allowed_module_prefixes parameter for restricting which
modules can be loaded
- Centralize class loading logic in get_step_class_from_qualified_name()
utility function
- Remove duplicate _get_class_from_string methods from DaprStepInfo and
StepActor
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raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
Overriding merge checks as there are unrelated Python integration test failures
### Motivation and Context
- Uploads: Require both EnableDangerousFileUploads = true AND
AllowedUploadDirectories configured
- Downloads: Permissive by default, but if AllowedDownloadDirectories is
configured, path validation
applies
- Both use path canonicalization to prevent traversal attacks
### Contribution Checklist
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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
### Motivation and Context
When `SessionsPythonTool` is used with an AI model, providing explicit
directory boundaries for file operations gives developers fine-grained
control over which parts of the filesystem the plugin can access. This
follows the pattern established by `HttpPlugin.allowed_domains`.
- Add `allowed_upload_directories` parameter to control which local
directories can be used for file uploads
- Add `allowed_download_directories` parameter for optional restrictions
on file download destinations
- Implement path canonicalization to resolve symlinks and normalize
paths
Usage:
```python
tool = SessionsPythonTool(
credential=credential,
allowed_upload_directories={"/app/data/uploads", "/app/user_files"},
allowed_download_directories={"/app/data/downloads"}, # optional
)
```
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### Description
- `upload_file` now requires `allowed_upload_directories` to be
configured (deny-by-default)
- `download_file` optionally supports `allowed_download_directories`
(permissive-by-default, since it's not exposed as a kernel function)
- Both parameters accept `set[str]` or `list[str]` for convenience
- Paths are canonicalized using `os.path.realpath()` before validation
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Adds allowed_domains setup for HttpPlugin.
### Description
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### Motivation and Context
Bump Python version to 1.39.2 for a release
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### Description
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Fixes some issues with the forbidden list approach for the in-memory
vector store filtering.
### Description
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### Motivation and Context
Bump to v1.39.1 for a release
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Adding the OracleCollection to the all for oracle
### Description
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Slight improvement to the in-memory vector store filtering.
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## Summary
Fixes#13349
When loading agents from YAML files using
`AgentRegistry.create_from_file()` or `create_from_yaml()`, the
`model.options` field (containing settings like `response_format`,
`temperature`, etc.) were being added as regular `KernelArguments` dict
items instead of being placed in the `execution_settings` property. This
caused AI service calls to ignore these critical settings.
## Root Cause
In `_normalize_spec_fields()` method in
`python/semantic_kernel/agents/agent.py`, model options were being
passed to `KernelArguments` constructor as `**kwargs`:
```python
# OLD (buggy) CODE
arguments = KernelArguments(**model_options)
```
This made them regular dict items, but execution settings need to be in
the `execution_settings` property to be passed to AI services.
## Changes
Modified `_normalize_spec_fields()` in
`python/semantic_kernel/agents/agent.py`:
- Added import for `PromptExecutionSettings`
- Convert model.options to `PromptExecutionSettings` object before
creating `KernelArguments`
- Model options now correctly placed in `execution_settings` property
- Input defaults continue to be added as regular dict items (correct
behavior)
## Testing
- ✅ Created custom test verifying model.options are now in
execution_settings
- ✅ All 30 existing unit tests in `tests/unit/agents/test_agent.py` pass
- ✅ No breaking changes introduced
- ✅ Verified execution_settings passed programmatically still work
correctly
## Example
YAML file:
```yaml
type: chat_completion_agent
name: TestAgent
description: Test agent
instructions: You are a test agent
model:
options:
response_format: structured_output
temperature: 0.7
```
**Before this fix:**
- `response_format` was in dict items (wrong location)
- AI service calls ignored it
**After this fix:**
- `response_format` is in `execution_settings.extension_data`
- AI service calls receive it properly
…ync support
### Motivation and Context
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This change is required to enable Semantic Kernel users to store and
retrieve embeddings using Oracle databases. Currently, Semantic Kernel
supports vector storage for several backends, but Oracle was missing.
This connector solves that gap by providing full async support, native
VECTOR type handling, and vector index management.
### Description
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This PR introduces a new Oracle connector for Semantic Kernel with the
following features:
- Asynchronous upsert, get, delete and search operations for memory
records.
- Native Oracle VECTOR type support for storing embeddings efficiently.
- Support for HNSW and IVFFLAT vector indexes for similarity search.
- Integration with Semantic Kernel collections, enabling semantic search
and memory operations.
- Comprehensive unit tests to ensure correctness and stability.
The connector is designed to work seamlessly with existing Semantic
Kernel memory abstractions and follows the same async patterns as other
vector stores.
Integration tests have also been implemented and verified locally;
however, they are not included in this PR because the current CI
environment setup for Oracle Database support is unknown.
Once guidance is provided on Oracle DB availability in the CI pipeline,
integration tests can be enabled and added in a follow-up PR.
### Contribution Checklist
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### Motivation and Context
Bump to v1.39.0 for a release
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### Description
Bump to v1.39.0 for a release
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Closes#12970, #13352
### Description
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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
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### Motivation and Context
Bump Python version to 1.38.0 for a release.
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### Description
Bump Python version to 1.38.0 for a release.
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### Motivation and Context
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OTel GenAI operation names have the following updates:
1. `chat.completion` to `chat`
2. `text.completion` to `text_completion`
### Description
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This PR updates those operation names
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Adds a new method to KernelFunction that creates a Agent Framework Tool
from that function.
Does require the user to install agent-framework-core themselves.
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### Motivation and Context
Bump Python version to 1.37.1 for a release.
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### Description
Bump Python version to 1.37.1 for a release.
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## Summary
Fixed an issue where the `text` parameter was missing in requests to the
OpenAI Responses API.
## Changes Made
Modified the `ResponsesAgentThreadActions._generate_options()` method to
correctly handle the `text` parameter:
- Added to the options dictionary **only** if `text` is not `None`.
- Not added if it is an empty dict (the default value).
- This ensures that **only explicitly set `text` parameters** are sent
to the API.
## Fix Location
**File:**
`semantic_kernel/agents/open_ai/responses_agent_thread_actions.py`
**Before:**
```python
options = {
"model": merged.get("model"),
"top_p": merged.get("top_p"),
"temperature": merged.get("temperature"),
"truncation": truncation,
"metadata": merged.get("metadata"),
"max_output_tokens": max_output_tokens,
"parallel_tool_calls": parallel_tool_calls,
}
```
**After:**
```python
options = {
"model": merged.get("model"),
"top_p": merged.get("top_p"),
"temperature": merged.get("temperature"),
"truncation": truncation,
"metadata": merged.get("metadata"),
"max_output_tokens": max_output_tokens,
"parallel_tool_calls": parallel_tool_calls,
}
# Add text option if it exists
if merged.get("text") is not None:
options["text"] = merged.get("text")
```
- Closes#13281
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---------
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
Fixes#13199 - Non-string kernel arguments are now preserved when passed
to functions in prompt templates. Adds unit tests.
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Vertex AI and Bedrock integration tests have been failing.
### Description
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1. This PR fixes the vertex ai integration tests by upgrading the Google
AI SDK version.
2. This PR fixes the Bedrock integration tests by upgrading the models.
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---------
Co-authored-by: Copilot <[email protected]>
Co-authored-by: moonbox3 <[email protected]>
### Motivation and Context
For the latest onnxruntime 1.23.2 pkg: our GitHub Actions run is failing
because uv is not matching the macOS wheel tag.
Pinning to last-known-good until it's sorted out.
- Pin openai pkg < 2.0 until we can investigate if 2.0 brings in other
breaking changes.
### Contribution Checklist
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### Motivation and Context
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- 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
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### Description
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- 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
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### Motivation and Context
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The current GenAI Semantic Conventions have changed since the last time
we modified the `agent_invoke` span.
Previous PRs: #12940, #12928, #12834
### Description
The following changes were made:
1. `gen_ai.agent.invocation_input` -> `gen_ai.input.messages`
2. `gen_ai.agent.invocation_output` -> `gen_ai.output.messages`
3. Add `gen_ai.tool.definitions`
.Net:
<img width="2056" height="459" alt="image"
src="https://github.com/user-attachments/assets/533b8e08-7674-4c70-bdbe-ba3600855b20"
/>
Python:
<img width="1323" height="659" alt="image"
src="https://github.com/user-attachments/assets/702171ac-dc0b-4187-933d-dad77f2719df"
/>
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### Motivation and Context
Bump Python version to 1.37.0 for a release.
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### Description
Bump Python version to 1.37.0 for a release.
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### Motivation and Context
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Enables developers to use NVIDIA's chat models through Semantic Kernel
with structured output support.
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This change adds chat completion support with structured output to the
existing NVIDIA connector, enabling developers to use NVIDIA's chat
models through Semantic Kernel while building conversational AI
applications that require structured, validated responses. The connector
will support more model types iteratively, with future PRs introducing
VLM models and additional capabilities.
### Description
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- New Chat Completion Service:
`NvidiaChatCompletion`: Chat completion service following same structure
as other connectors
- Extends existing NVIDIA embedding connector patterns and architecture
- Custom logic for handling structured output specific to NVIDIA models
- Enhanced Configuration:
`NvidiaChatPromptExecutionSettings`: Chat-specific settings following
existing connector patterns
### Contribution Checklist
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- [x] The code builds clean without any errors or warnings
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- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
### Motivation and Context
Currently, we attempt to deep copy plugins, no matter what the plugin
instance holds. This will cause issues when plugins contain unpickleable
items like an async generator.
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### Description
- Closes#12819
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### Contribution Checklist
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## Summary
Fixes vector search functionality in Cosmos DB NoSQL connector that was
failing with syntax errors and "One of the input values is invalid"
errors.
## Changes
- Fixed VectorDistance function call to use correct 2-parameter syntax
instead of 4 parameters
- Removed RANK keyword from ORDER BY clause to fix SQL syntax error
- Removed distance function parameter setting as it should be configured
in vector index, not query
- Added proper handling for empty where clauses
## Testing
- Verified fix resolves the syntax errors reported in issue #13028
- Vector search functionality now works correctly with existing
collections
Fixes#13028
---------
Co-authored-by: Jian Wang <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
Bump Python version to 1.36.2 for a release
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### Description
Bump Python version to 1.36.2 for a release
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### Motivation and Context
The `arguments: KernelArguments` are not getting handled properly for
the Responses Agent invoke. Use the provided `arguments` instead of
trying to get them from the kwargs.
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### Description
- Closes#13053
- Adds unit test coverage.
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### Motivation and Context
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Addresses #13005
### Description
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1. Remove the `extra_parameters` key from
`AzureAIInferenceChatPromptExecutionSettings` before calling the chat
completion API to avoid unknown parameter error.
2. Allow setting the API version on the `AzureAIInferenceXXX`
connectors.
### Contribution Checklist
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- [x] I didn't break anyone 😄
### Motivation and Context
Current, while using an OpenAIAssistantAgent, we are returning code
output via the on_intermediate_message callback. This shouldn't be
happening right now, as all other agents return code via TextContent
with a metadata `{"code": True}` key-value pair in the dictionary.
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### Description
- Closes#12956
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### Contribution Checklist
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Evan Mattson