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Author SHA1 Message Date
Dmytro Struk 18d9a8e8b2 Python: .Net: Updated encoding logic in prompt templates (#12983) 
### Motivation and Context

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Resolves: https://github.com/microsoft/semantic-kernel/issues/11821

Today, the encoding of template arguments is performed only if argument
type is `string`. In case of custom type, anonymous type or collection -
the encoding is not performed.

This PR contains changes to throw an exception in case if encoding is
enabled but complex type is used. In case of complex type, the encoding
should be performed manually according to business logic and automatic
encoding should be explicitly disabled.

This enforces stricter, but more secure template rendering rules.

**Note**: this is a breaking change for customers who use Handlebars or
Liquid template with complex type arguments. Code changes are required
when initializing template arguments:
```diff
var arguments = new KernelArguments()
{
    { "customer", new
        {
-             firstName = userInput.FirstName,
-             lastName = userInput.LastName,
+             firstName = HttpUtility.HtmlEncode(userInput.FirstName),
+             lastName = HttpUtility.HtmlEncode(userInput.LastName),
        }
    }
};

var templateFactory = new LiquidPromptTemplateFactory();
var promptTemplateConfig = new PromptTemplateConfig()
{
    TemplateFormat = "liquid"
+   InputVariables = new()
+   {
+       // We set AllowDangerouslySetContent to 'true' because each property of this argument is encoded manually. 
+       new() { Name = "customer", AllowDangerouslySetContent = true },
+   }
};

var promptTemplate = templateFactory.Create(promptTemplateConfig);
var renderedPrompt = await promptTemplate.RenderAsync(kernel, arguments); 
```

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- [ ] I didn't break anyone 😄
 2025-08-26 22:09:00 -07:00
Dmytro Struk 2a47ba9b7d Python: Removed usage of DefaultAzureCredential (#12964) 
### Motivation and Context

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The usage of `DefaultAzureCredential` by default is unsafe and raises
security issues. Instead, users should provide their own credential
types such as `AzureCliCredential`, `ManagedIdentityCredential` and so
on. More information here:

https://learn.microsoft.com/en-us/python/api/azure-identity/azure.identity.aio?view=azure-python

In this PR:
1. Removed the usage of `DefaultAzureCredential` from authentication
logic.
2. Replaced `DefaultAzureCredential` with `AzureCliCredential` in all
examples.

**Note**: this is breaking change for Azure users, who relies on
`DefaultAzureCredential`. Users will need to update the code and pass
the credential type of their choice by using `credential` parameter when
initializing one of the Azure clients.

Before:
```python
chat_completion = AzureChatCompletion()
```

After:
```python
chat_completion = AzureChatCompletion(credential=AzureCliCredential())
```

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- [ ] I didn't break anyone 😄
 2025-08-25 18:29:13 +00:00
Evan Mattson 3e31c3bdc6 Python: Remove planner-related code and samples. (#12417) 
### Motivation and Context

Per deprecation method, removing planner-related code after June 1,
2025.

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### Description

- Removing planner-related code.
- Closes #12400

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- [ ] I didn't break anyone 😄
 2025-06-09 21:35:23 +00:00
Evan Mattson ff1997047d Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247) 
### Motivation and Context

This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.

To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.

In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.

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### Description

Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979

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### Contribution Checklist

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raises no violations
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- [X] I didn't break anyone 😄
 2025-05-26 05:02:59 +00:00
Eduard van ValkenburgandEvan Mattson 0634c30241 Python: marked planners deprecated and removed all related items (#12058) 
### Motivation and Context

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In line with our dotnet, marking all planners as deprecated, and
removing tests and samples using them.

### Description

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- [ ] I didn't break anyone 😄

---------

Co-authored-by: Evan Mattson <[email protected]>
 2025-05-19 02:47:36 +00:00
Evan Mattson 7a19ae350e Python: Update AzureAIAgent sample (#11175) 
### Motivation and Context

Following the latest updates for the Common Agent Invocation API
feature, one AzureAIAgent sample was not configured properly.

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### Description

Update the sample so the response doesn't show twice. Should print the
agent name and then the response.

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 2025-03-25 10:13:31 +00:00
westeyBen ThomasEvan MattsonTao ChenBen ThomasMark WallaceEvan Mattson
 		17e63a782b Python/.NET: Add a common agent invoke api. (#11116) 
### Motivation and Context

Being able to different invoke agents using a common API is useful to
build higher level code on top of.

### Description

Adding a common Invoke method with a common Thread object.

### Contribution Checklist

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- [ ] I didn't break anyone 😄

---------

Co-authored-by: Ben Thomas <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Tao Chen <[email protected]>
Co-authored-by: Ben Thomas <[email protected]>
Co-authored-by: Mark Wallace <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
 2025-03-24 23:29:06 +00:00
Evan Mattson fd27470c86 Python: Allow plugins via agent constructors. Update samples. (#10707) 
### Motivation and Context

In Semantic Kernel, our goal is to simplify working with agents. When an
agent is created without a specified kernel, one is automatically
created on the user’s behalf. This enables developers to pass in a list
of plugins that are then added to the underlying kernel. If both the
agent constructor and the provided kernel include plugins, those
specified via the agent constructor take precedence—since plugins are
stored in a dictionary, any duplicate names result in the agent’s plugin
overwriting the existing one.

For the `ChatCompletionAgent`, usage is further simplified by allowing a
`ChatCompletionClientBase` service to be passed via the constructor. If
a kernel is supplied and already contains a chat completion service, the
new service is added to the kernel. Moreover, if no execution settings
are provided via KernelArguments, the first service registered on the
kernel is used.

New usage to create a ChatCompletionAgent

```python
agent = ChatCompletionAgent(
    service=AzureChatCompletion(),
    name="<name>",
    instructions="<instructions>",
)
```

New usage to create a ChatCompletionAgent with plugins:

```python
agent = ChatCompletionAgent(
    service=AzureChatCompletion(),
    name="<name>",
    instructions="<instructions>",
    plugins=[SamplePlugin()],
)
```

Previously, the `service_id` constructor argument was retained after
introducing KernelArguments. However, with our transition to using the
kernel’s AI service selector, the `service_id` parameter has become
redundant. As we move from the experimental phase toward a release
candidate, removing `service_id` is a necessary, albeit breaking,
change.

The `getting_started_with_agents/chat_completion` samples now begin by
demonstrating the simplest way to configure the agent. The subsequent
step illustrates the original method for setting up the chat completion
service on the kernel. Similarly, for plugins, the initial approach is
the easiest to follow, while the following step shows the traditional
method of managing the kernel.

Updated documentation will follow, once these changes are released in a
new package.

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### Description

Simplifying interactions with agents.
- Updated the `ChatCompletionAgent`, `AzureAssistantAgent`, and
`OpenAIAssistantAgent` classes with the `release_candidate` decorator in
place of the `experimental` decorator.
- Add a chat completion agent structured outputs sample
- Closes #10604

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- [ ] I didn't break anyone 😄
 2025-02-28 01:39:36 +00:00
Evan Mattson 9e979f8463 Python: Improve/Simplify the Assistant Agents (#10666) 
### Motivation and Context

The current abstractions we have around the Semantic Kernel Assistant
Agents are considered to be "fully-wrapped." This is beneficial in that
SK provides a tight/complete abstraction over the agent; however, it
doesn't scale well when new features are added to the underlying APIs
(OpenAI) - it requires updates from SK to make each and every update. It
also adds a lot more complexity in terms of needing to handle everything
in SK, requires more code coverage, and chances for bugs to surface due
to these complexities.

> **NOTE:** this update is _breaking_. We're moving to simplify the
abstractions/APIs before we move Assistant Agents from **experimental**
to GA.

Similar to how the `AzureAIAgent` was created, we're moving to a
"less-wrapped" abstraction around the OpenAI Assistant v2 APIs. We do
have some "convenience methods" on the Agents, via class methods, to
configure things like the code interpreter tool and resource, file
search tool and resource, the clients required to interact with the
Assistants, as well as being able to easily configure structured
outputs, similar to how it is done with a ChatCompletion (either provide
the Pydantic model or the Python class).

To work with Assistant Agents now, it's as simple as:

```python
from semantic_kernel.agents.open_ai import AzureAssistantAgent
from semantic_kernel.functions import kernel_function

class MenuPlugin:
   # Plugin code here using the @kernel_function decorator

# Create the client using Azure OpenAI resources and configuration
client, model = AzureAssistantAgent.setup_resources()

# Create the assistant definition
definition = await client.beta.assistants.create(
    model=model,
    instructions="Answer questions about the menu.",
    name="Host",
)

agent = AzureAssistantAgent(
    client=client,
    definition=definition,
    plugins=[MenuPlugin()],  # The plugins can be passed in as a list to the constructor
)

# Note: plugins can also be configured on the Kernel and passed in as a parameter to the OpenAIAssistantAgent

# Define a thread and invoke the agent with the user input
thread = await agent.client.beta.threads.create()
```

**I will create a migration guide (issue) showing what needs to be
updated from the previous usage to this new pattern.**

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### Description

This PR provides the new abstractions for the
`AzureAssistantAgent`/`OpenAIAssistantAgent` classes.
- Updates underlying code
- Updates samples showing new patterns
- Updates unit tests
- Updates READMEs.
- Closes #10605 

Samples will be updated accordingly to show new patterns.

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- [ ] I didn't break anyone 😄
 2025-02-26 10:59:14 +00:00
Evan Mattson 784f13e400 Python: Fix Chat History Agent format instructions regression. (#10512) 
### Motivation and Context

The chat history agent templating was inadvertently broken. This PR
restores the functionality to be able to use templating with a Chat
Completion Agent.

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### Description

Fix the templating for a Chat Completion Agent. Add unit tests for more
coverage.
- Fix a unit test fixture that was annotated to return an AsyncMock, but
it's actually returning an async generator function.

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 2025-02-13 11:53:21 +00:00
Eduard van Valkenburg cbfd7e9330 Python: improved content inits, added ndarray support for binary content and small fixes to defaults (#10469) 
### Motivation and Context

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This PR adds support for ndarray's as the content carrier for all binary
content types (binary, image, audio) as that is more optimized for
larger content.

It also does some fixes to the initialization of those content types and
the underlying data uri type.

Also some fixes for unspecified default param in pydantic Field,
language servers do not recognize `Field("default value")` as having a
default, so changed those occurances to `Field(default="default value")`

### Description

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raises no violations
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- [x] I didn't break anyone 😄
 2025-02-12 16:59:08 +00:00
Evan Mattson dc6ca1afad Python: Fix csv file paths for learn site sample. Improve streaming output formatting with code present (#10498) 
### Motivation and Context

The code sample located
[here](https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/examples/example-assistant-code?pivots=programming-language-python)
had an issue loading the resource csv files right out of the box.
Additionally, the formatted output could use some help.

Now the output is formatted nicely like:

---
To provide a column chart of the top 10 countries by population with
values at the top of each column, I'll need to examine the contents of
the uploaded files. Let's first take a look at the data in these files
to understand their structure and content.

```python
import pandas as pd

# Load the uploaded files to inspect their contents
file_1_path = '/mnt/data/assistant-T1tWYT1f5Y8JFRuYtxgjck'
file_2_path = '/mnt/data/assistant-LUgpebvSh4PTrFdEkVEXMi'

# Try reading the files into pandas dataframes
try:
    data_1 = pd.read_csv(file_1_path)
except Exception as e:
    data_1 = str(e)

try:
    data_2 = pd.read_csv(file_2_path)
except Exception as e:
    data_2 = str(e)

data_1, data_2
```

The first file seems to contain regional population data for various
provinces and states within countries, such as Belgium and the US. The
important columns for this task are:
- `Country_Region`: the name of the country
- `Population`: the population of the region

The second file provides population data by country, which seems more
appropriate for finding the top 10 countries by population. It contains:
- `Country_Region`: the name of the country
- `Population`: the population of the country

We'll use the second file to extract the top 10 countries by population
and create a column chart with the population values displayed on top.
Let's proceed with this analysis.

```python
import matplotlib.pyplot as plt
import seaborn as sns

# Use the second data set which has per country data
country_data = data_2

# Sort by Population and select the top 10 countries
top_10_countries = country_data.sort_values(by='Population', ascending=False).head(10)

# Plotting
plt.figure(figsize=(12, 8))
sns.barplot(data=top_10_countries, x='Country_Region', y='Population', palette='viridis')
plt.xticks(rotation=45)
plt.title('Top 10 Countries by Population')
plt.xlabel('Country')
plt.ylabel('Population')

# Adding population values on top of each bar
for index, row in top_10_countries.iterrows():
    plt.text(index, row['Population'], f"{row['Population']:,}", color='black', ha="center")

plt.tight_layout()
plt.show()

```
---

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### Description

Fix formatting and csv links for a learn resource sample.
- Will update the learn site after this PR is approved.
- Although not directly related to the server-side errors from the Azure
Assistant Agent, this issue is related: #10482


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- [X] I didn't break anyone 😄
 2025-02-12 15:40:56 +00:00
Evan Mattson 1a84cb64b2 Python: prompt template config and kernel args support for agents (#10298) 
### Motivation and Context

To bring the SK Python framework closer to .Net parity, we need to
support the ability to specify a prompt template config and kernel
arguments for ChatCompletion Agents as well as for Assistant Agents.

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### Description

This PR adds support for:
- being able to specify a prompt template config for SK agents.
- allows kernel arguments to be passed to the agent as well as during
invoke - invoke args, similar to other kwargs, take precedence over
agent level config.
- adds test coverage
- creates agent templating samples for both ChatCompletionAgents and
OpenAI/AzureAssistantAgents
- closes #8022 
- closes #8699

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- [X] I didn't break anyone 😄
 2025-01-28 04:41:16 +00:00
Evan Mattson 16690ed857 Python: Yield FunctionResultContent in streaming chat completion path. Update tests. (#9974) 
### Motivation and Context

Currently, if using SK's Python streaming chat completion path, we only
yield the following content types: `StreamingChatMessageContent` and
`FunctionCallContent`. We are not yielding `FunctionResultContent` which
is valuable for some use cases.

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### Description

This PR updates the code to yield `FunctionResultContent` if it exists
in the streaming chat completion path. When we merge the function call
result content together into a `StreamingChatMessageContent` type, we
check if that message has items (which are of type
`FunctionResultContent`) and if so, we yield them. The filter path still
works because once they're yielded, we break out of the function calling
loop.

We need to include the `ai_model_id` if it exists for the current
PromptExecutionSettings because if performing a `reduce` operation to
add two streaming chat message chunks together, the
`StreamingChatMessageContent` that has the function results will break
if the `ai_model_id` is not set (the error is thrown in the `__add__`
override for the `StreamingChatMessageContent`.

Some unit tests that cover function calling were also updated -- during
the test, the test JSON function args were breaking in the `json.loads`
call because they contained single quotes and not double. We're now
sanitizing the args, just in case, so we don't break there.

This PR fixes:
- #9408
- #9968 

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 2024-12-18 01:09:25 +00:00
Eduard van Valkenburg b1f4769ba5 Python: updated pydantic 2.10.3 works again (#9923) 
### Motivation and Context

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Manual update to pydantic requirement, they fixed the incompatibility in
10.3 (10.<3 was broken).

We need one more fix in Pydantic 2.10, which should come in 2.10.4:
https://github.com/pydantic/pydantic/issues/10910
this is the issue: https://github.com/pydantic/pydantic/issues/11024
Comes into play with some of the vector stores.

So excluded 2.10 smaller then 2.10.4.

Closes #9788

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 2024-12-12 21:08:46 +00:00
Eduard van ValkenburgandEvan Mattson 93bf06ed3c Python: updated pre-commits (#9739) 
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Put some of the pre-commit steps to their latest versions.
Improve the setup of the uv lock hook.

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Co-authored-by: Evan Mattson <[email protected]>
 2024-11-19 16:36:01 +00:00
Elizabeth HalperandEvan Mattson ec9233cc34 Python: Fix AI Search Environment Variables (#9160) 
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Updating the AI Search environment variables to follow convention and
match the code as it was throwing an error and unable to connect to the
service as it was. It also loads in the environment properly using the
dotenv package.

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 2024-10-11 13:38:33 +00:00
ChrisandEvan Mattson 60d555e7e7 Python/.Net: Agent Resources for Learn-Site Update (#9104) 
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Learn Site Resources for Agent Framework docs 

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Resources and code associated with examples

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 2024-10-08 15:57:57 +00:00
Chris Vaisnor ac1613aa1c Python: removed underscore in .env for open_ai (#7625) 
### Description

When running the getting-started Python notebooks, the user is
instructed to create and populate their own .env file. The values for
the OpenAI specifications included an underscore between 'open' and
'ai'. This led to an error when initializing the chat/text/embedding
model service.

I have removed the underscore from the base .env files, README, and in
the evaluate_with_prompt_flow.py script when reading from the .env file.
 2024-08-02 21:01:01 +00:00
Evan Mattson 8381e50e8b Python: Introduce the new function calling abstraction, FunctionChoiceBehavior (#6910) 
### Motivation and Context

The current `FunctionCallBehavior` has allowed us to utilize auto
function calling for OpenAI type models. As we proceed to support more
AI connectors, that differ from OpenAI models, we need to be able to
handle functions for all models that support function calling.

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### Description

In this PR:
- We introduce a new function calling abstraction called
`FunctionChoiceBehavior` which has three types: Auto, Required, and
None.
- One is able to configure the `function_choice_behavior` along with
fully qualified function names (e.g., plugin1.function1),
`maximum_auto_invoke_attempts` or `auto_invoke_kernel_functions` in yaml
and JSON prompts. We have a new concept example showing how to do this
for for yaml and JSON prompts.
- If the fully qualified names are specified in the config file, they
take precedence over the filters, if specified at a later point.
- To make sure this isn't a breaking change, we still handle the
previous `FunctionCallBehavior`, but so we can make decisions on the new
`FunctionChoiceBehavior` we map the `FunctionCallBehavior` to
`FunctionChoiceBehavior`. Each time `FunctionCallBehavior` is updated,
`FunctionChoiceBehavior` will be updated, too.
- The `_process_tool_call()` method in the
`open_ai_chat_completion_base` needs to maintain the argument name as
`function_call_behavior` as we cannot introduce a breaking change.
- All concept samples have been converted to use
`FunctionChoiceBehavior`.
- New unit tests have been added for `FunctionChoiceBehavior` and we
still currently support `FunctionCallBehavior` tests to make sure we
haven't broken the backwards compatibility.
- Added `deprecated` typing decorators to classes/methods to alert users
that it would be best to transition to `FunctionChoiceBehavior` even
though `FunctionCallBehavior` is still supported.
- The `FunctionCallingStepwisePlanner` was updated to use the new
`FunctionChoiceBehavior`.
- Closes #6496, #6458
- Addresses #6626 

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 2024-06-27 20:34:59 +00:00
Eduard van Valkenburg 05dcb29dea Python: Support for image content and chat message content with image content (#6520) 
### Motivation and Context

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Introduces a ImageContent class
Allows that type to be part of ChatMessageContent
Adds a sample for both local and online images
Adds many tests with data uri edge cases, and binary parsing edge cases.

Closes #6536 

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 2024-06-13 15:09:25 +00:00
Evan Mattson a2720a605d Python: Fix broken concept samples. Run concept examples as tests. (#6660) 
### Motivation and Context

We had many broken concept examples. These samples are also not covered
by tests so it's hard to know when things are out of sync.

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### Description

Fix the Python concept samples, and also cover them with tests so we
aren't checking in code that is going to break them.

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 2024-06-11 15:28:12 +00:00
Evan Mattson 382972b666 Python: Adjust the learn file imports based on a previous refactor. (#6545) 
### Motivation and Context

Several of the imports in the learn files were showing as unable to
resolve after the service configuration was moved to a different
directory.

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### Description

Creating the `__init__.py` in the directory where the importable files
live. Adjusting code to reflect this. Imports now resolve.

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 2024-06-04 19:40:12 +00:00
Eduard van Valkenburg fb8edbef94 Python: updates to cicd and notebooks (#6485) 
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Small update to test coverage workflow, to retrigger based on workflow
run.
Small updates to samples notebooks, to use GLOBAL_LLM_SERVICE allowing
control of where they run through env.

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 2024-06-03 13:58:02 +00:00
Evan Mattson 9e46e52c97 Python: Fix learn resource planner sample (#6478) 
### Motivation and Context

The learn resource planner sample was not always passing as the plan
created was non-deterministic.

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### Description

Update the planner sample to run properly by introducing the Function
Calling Stepwise Planner. Also introduce a small Pydantic Settings class
to remove the dependency on the dotenv library.

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 2024-06-03 06:32:40 +00:00
Eduard van Valkenburg fbb26b5cf6 Python: further improvements to checks and docs in dev_setup (#6445) 
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Added a section on documentation to the DEV_SETUP
Added a check for the copyright row to pre-commit
Closes #5629

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 2024-05-31 13:06:41 +00:00
Eduard van ValkenburgandEvan Mattson 172e93113e Python: updated samples (#6411) 
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Updated samples for MS Learn, with tags.

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Co-authored-by: Evan Mattson <[email protected]>
 2024-05-28 15:46:18 +00:00
Eduard van Valkenburg 9e99933a27 Python: new pre-commit actions and pre-commit as GHA (#6376) 
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- Added static code checking to pre-commit (check-ast and
nbqa-check-ast)
- Added Bandit security checking to pre-commit
- Added pre-commit step to python-lint workflow, if it works, can delete
seperate mypy, ruff and black tests

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 2024-05-28 15:28:04 +00:00
StefanandEvan Mattson a9d7d5d90f Python: Refactoring. Use get_function and get_plugin. (#6382) 
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Refactoring some of the code following [this
advice](https://github.com/microsoft/semantic-kernel/pull/6371#discussion_r1611138252)
from the maintainers.

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Look up kernel functions and plugins with the `get_function` and
`get_plugin` method. Removed the unused `DEFAULT_CHAT_SYSTEM_PROMPT`

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Co-authored-by: Evan Mattson <[email protected]>
 2024-05-24 06:02:18 +00:00
Eduard van Valkenburg 81cdde24fc Python: upgraded all files to 310 plus format and removed from future (#6353) 
### Motivation and Context

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Now that we only support 310 and up, we could unify all files, to use
the new style typing and annotations.

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 2024-05-21 16:33:08 +00:00
Evan Mattson ce2d9c9b5f Python: Add PF learn path resources (#6122) 
### Motivation and Context

Add files that weren't staged to PF resources.

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### Description
Add files that weren't staged to PF resources.
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 2024-05-04 08:15:22 -04:00
Evan Mattson 09508dc8ba Python: Restructure samples into new folders to make things more clear. (#6116) 
### Motivation and Context

All previous samples were either in kernel syntax or a separate
notebooks folder. The goal is to make the samples easier to understand
and have a better structure.

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### Description

The PR restructures the kernel syntax examples into new folders:
concepts (with subfolders for previous syntax examples), demos,
getting_started, and learn_resources.
- Closes #6119 
- Adds a new concept/function example for understanding kernel
arguments.
- Updates the bookings plugin

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 2024-05-04 11:50:04 +00:00