Update the outdated ollama usage doc.

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## Why are these changes needed?

(Partially?) fixes accessibility issue (19). Question out to
accessibility team whether its enough.

Migrating to 16.0 for accessibility fixes. Not moving to 16.1 yet
because of a weird change to the 'Show Source' link's appearance

## Related issue number

#5630 

## Checks

- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
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Co-authored-by: Ryan Sweet <rysweet@microsoft.com>

Fix issue here in this discussion -
https://github.com/microsoft/autogen/discussions/4208#discussioncomment-12394408

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## Why are these changes needed?

Fix bug in AGS UI where frontend crashes because the default team config
is null

- update /teams endpoint to always return a default team if none is
found for the user
- update UI to check for team before rendering 
- also update run_id type to be autoincrement int (similar to team id)
instead of uuid. This helps side step the migration failed errors
related to UUID type when using an sqlite backend

<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->

## Checks

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build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

---------

Co-authored-by: Ryan Sweet <rysweet@microsoft.com>

Fix a warning that has been resolved in the latest release.

Added notice for user who are not using Jypter Notebook for the example,
to wrap with asyncio

## Why are these changes needed?

Keyboard focus location was being lost after a copy event. Header anchor
was also not selectable while hidden

Fixes (2), (4), (11), (35)

## Related issue number

#5630 

## Checks

- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
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introduced in this PR.
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_(EXPERIMENTAL, RESEARCH IN PROGRESS)_

In 2023 AutoGen introduced [Teachable
Agents](https://microsoft.github.io/autogen/0.2/blog/2023/10/26/TeachableAgent/)
that users could teach new facts, preferences and skills. But teachable
agents were limited in several ways: They could only be
`ConversableAgent` subclasses, they couldn't learn a new skill unless
the user stated (in a single turn) both the task and how to solve it,
and they couldn't learn on their own. **Task-Centric Memory** overcomes
these limitations, allowing users to teach arbitrary agents (or teams)
more flexibly and reliably, and enabling agents to learn from their own
trial-and-error experiences.

This PR is large and complex. All of the files are new, and most of the
added components depend on the others to run at all. But the review
process can be accelerated if approached in the following order.
1. Start with the [Task-Centric Memory
README](https://github.com/microsoft/autogen/tree/agentic_memory/python/packages/autogen-ext/src/autogen_ext/task_centric_memory).
1. Install the memory extension locally, since it won't be in pypi until
it's merged. In the `agentic_memory` branch, and the `python/packages`
directory:
        - `pip install -e autogen-agentchat`
        - `pip install -e autogen-ext[openai]`
        - `pip install -e autogen-ext[task-centric-memory]`
2. Run the Quickstart sample code, then immediately open the
`./pagelogs/quick/0 Call Tree.html` file in a browser to view the work
in progress.
    3. Click through the web page links to see the details.
2. Continue through the rest of the main README to get a high-level
overview of the architecture.
3. Read through the [code samples
README](https://github.com/microsoft/autogen/tree/agentic_memory/python/samples/task_centric_memory),
running each of the 4 code samples while viewing their page logs.
4. Skim through the 4 code samples, along with their corresponding yaml
config files:
    1. `chat_with_teachable_agent.py`
    2. `eval_retrieval.py`
    3. `eval_teachability.py`
    4. `eval_learning_from_demonstration.py`
    5. `eval_self_teaching.py`
6. Read `task_centric_memory_controller.py`, referring back to the
previously generated page logs as needed. This is the most important and
complex file in the PR.
7. Read the remaining core files.
    1. `_task_centric_memory_bank.py`
    2. `_string_similarity_map.py`
    3. `_prompter.py`
8. Read the supporting files in the utils dir.
    1. `teachability.py`
    2. `apprentice.py`
    3. `grader.py`
    4. `page_logger.py`
    5. `_functions.py`

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## Why are these changes needed?

Adds sidebar and breadcrumb selection and focus indicators to high
contrast modes.
Fixes (46), (55), (56)

## Related issue number

 #5633 

This change has no affect on normal color modes, but adds selection and
focus indicators to high contrast modes. I'm not sure how to get rid of
the double bars on nested links, but thats a minor issue

before:

![image](https://github.com/user-attachments/assets/62e7ce65-c3f0-4160-8260-b4153a4a2835)

after:

![image](https://github.com/user-attachments/assets/581ed2a2-5f7c-43c8-a675-2f6c57c6a251)

## Why are these changes needed?

Current webpage theme does not highlight code output boxes. Issues (5)
and (29)

## Related issue number

#5630 

## Checks

- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

Resolves #5786

Also updated the termination tutorial to include an example of a custom
termination conditon.

Also added to guide about FunctionTool and MCP tools.

Resolves #5777

## Why are these changes needed?

The current installation command fails in certain shells (e.g., `zsh`,
`fish`) because brackets (`[]`) are interpreted as special characters.
Adding quotes ensures compatibility across different environments,
including Linux, macOS, and Windows.

## Related issue number

No related issue, but this fixes an installation issue encountered by
multiple users.

## Checks

- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

## Why are these changes needed?

* `python3` to `python`: Windows uses `python` for Python 3 by default,
not `python3`.
* `bin` to `scripts`: Windows virtual environments use `Scripts` instead
of `bin`.

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

AutoGen was passing raw dictionaries to functions instead of
constructing Pydantic model or dataclass instances. If a tool function’s
parameter was a Pydantic BaseModel or a dataclass, the function would
receive a dict and likely throw an error or behave incorrectly (since it
expected an object of that type).

This PR addresses problem in AutoGen where tool functions expecting
structured inputs (Pydantic models or dataclasses) were receiving raw
dictionaries. It ensures that structured inputs are automatically
validated and instantiated before function calls. Complete details are
in Issue #5736

[Reproducible Example Code - Failing
Case](https://colab.research.google.com/drive/1hgoP-cGdSZ1-OqQLpwYmlmcExgftDqlO?usp=sharing)
 
<!-- Please give a short summary of the change and the problem this
solves. -->
## Changes Made:
- Inspect function signatures for Pydantic BaseModel and dataclass
annotations.
- Convert input dictionaries into properly instantiated objects using
BaseModel.model_validate() for Pydantic models or standard instantiation
for dataclasses.
  - Raise descriptive errors when validation or instantiation fails.
  - Unit tests have been added to cover all scenarios

Now structured inputs are automatically validated and instantiated
before function calls.

- **Updated Conversion Logic:**  
In the `run()` method, we now inspect the function’s signature and
convert input dictionaries to structured objects. For parameters
annotated with a Pydantic model, we use `model_validate()` to create an
instance; for those annotated with a dataclass, we instantiate the
object using the dataclass constructor. For example:

  ```python
  # Get the function signature.
  sig = inspect.signature(self._func)
  raw_kwargs = args.model_dump()
  kwargs = {}

  # Iterate over the parameters expected by the function.
  for name, param in sig.parameters.items():
      if name in raw_kwargs:
          expected_type = param.annotation
          value = raw_kwargs[name]
# If expected type is a subclass of BaseModel, perform conversion.
if inspect.isclass(expected_type) and issubclass(expected_type,
BaseModel):
              try:
                  kwargs[name] = expected_type.model_validate(value)
              except ValidationError as e:
                  raise ValueError(
f"Error validating parameter '{name}' for function
'{self._func.__name__}': {e}"
                  ) from e
          # If it's a dataclass, instantiate it.
          elif is_dataclass(expected_type):
              try:
cls = expected_type if isinstance(expected_type, type) else
type(expected_type)
                  kwargs[name] = cls(**value)
              except Exception as e:
                  raise ValueError(
f"Error instantiating dataclass parameter '{name}' for function
'{self._func.__name__}': {e}"
                  ) from e
          else:
              kwargs[name] = value
  ```

- **Error Handling Improvements:**  
Conversion steps are wrapped in try/except blocks to raise descriptive
errors when instantiation fails, aiding in debugging invalid inputs.

- **Testing:**  
Unit tests have been added to simulate tool calls (e.g., an `add` tool)
to ensure that with input like:
  ```json
  {"input": {"x": 2, "y": 3}}
  ```
The tool function receives an instance of the expected type and returns
the correct result.


## Related issue number
Closes #5736
 
## Checks
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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.

Correcting an error: If CodeReviewResult is not approved, the coder
agents sends a CodeReviewTask back to the reviewer agent, not a
CodeWritingTask.

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

Starting out this draft PR to add documentation for the
`with_requirements` decorator in the `autogen-core` package.

---------

Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>

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## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
solves. -->

The PR introduces two changes.

The first change is adding a name attribute to
`FunctionExecutionResult`. The motivation is that semantic kernel
requires it for their function result interface and it seemed like a
easy modification as `FunctionExecutionResult` is always created in the
context of a `FunctionCall` which will contain the name. I'm unsure if
there was a motivation to keep it out but this change makes it easier to
trace which tool the result refers to and also increases api
compatibility with SK.

The second change is an update to how messages are mapped from autogen
to semantic kernel, which includes an update/fix in the processing of
function results.

## Related issue number

<!-- For example: "Closes #1234" -->

Related to #5675 but wont fix the underlying issue of anthropic
requiring tools during AssistantAgent reflection.

## Checks

- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

---------

Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>

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## Why are these changes needed?

Make FileSurfer and CodeExecAgent Declarative.
These agent presents are used as part of magentic one and having them
declarative is a precursor to their use in AGS.

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solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->
Closes #5607

## Checks

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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

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## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
solves. -->
Shows an example of how to use the `Memory` interface to implement a
just-in-time vector memory based on chromadb.

```python
import os
from pathlib import Path

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_core.memory import MemoryContent, MemoryMimeType
from autogen_ext.memory.chromadb import ChromaDBVectorMemory, PersistentChromaDBVectorMemoryConfig
from autogen_ext.models.openai import OpenAIChatCompletionClient

# Initialize ChromaDB memory with custom config
chroma_user_memory = ChromaDBVectorMemory(
    config=PersistentChromaDBVectorMemoryConfig(
        collection_name="preferences",
        persistence_path=os.path.join(str(Path.home()), ".chromadb_autogen"),
        k=2,  # Return top  k results
        score_threshold=0.4,  # Minimum similarity score
    )
)
# a HttpChromaDBVectorMemoryConfig is also supported for connecting to a remote ChromaDB server

# Add user preferences to memory
await chroma_user_memory.add(
    MemoryContent(
        content="The weather should be in metric units",
        mime_type=MemoryMimeType.TEXT,
        metadata={"category": "preferences", "type": "units"},
    )
)

await chroma_user_memory.add(
    MemoryContent(
        content="Meal recipe must be vegan",
        mime_type=MemoryMimeType.TEXT,
        metadata={"category": "preferences", "type": "dietary"},
    )
)


# Create assistant agent with ChromaDB memory
assistant_agent = AssistantAgent(
    name="assistant_agent",
    model_client=OpenAIChatCompletionClient(
        model="gpt-4o",
    ),
    tools=[get_weather],
    memory=[user_memory],
)

stream = assistant_agent.run_stream(task="What is the weather in New York?")
await Console(stream)

await user_memory.close()
```

```txt
 ---------- user ----------
What is the weather in New York?
---------- assistant_agent ----------
[MemoryContent(content='The weather should be in metric units', mime_type='MemoryMimeType.TEXT', metadata={'category': 'preferences', 'mime_type': 'MemoryMimeType.TEXT', 'type': 'units', 'score': 0.4342913043162201, 'id': '8a8d683c-5866-41e1-ac17-08c4fda6da86'}), MemoryContent(content='The weather should be in metric units', mime_type='MemoryMimeType.TEXT', metadata={'category': 'preferences', 'mime_type': 'MemoryMimeType.TEXT', 'type': 'units', 'score': 0.4342913043162201, 'id': 'f27af42c-cb63-46f0-b26b-ffcc09955ca1'})]
---------- assistant_agent ----------
[FunctionCall(id='call_a8U3YEj2dxA065vyzdfXDtNf', arguments='{"city":"New York","units":"metric"}', name='get_weather')]
---------- assistant_agent ----------
[FunctionExecutionResult(content='The weather in New York is 23 °C and Sunny.', call_id='call_a8U3YEj2dxA065vyzdfXDtNf', is_error=False)]
---------- assistant_agent ----------
The weather in New York is 23 °C and Sunny.
```

Note that MemoryContent object in the MemoryQuery events have useful
metadata like the score and id retrieved memories.

## Related issue number

<!-- For example: "Closes #1234" -->


## Checks

- [ ] I've included any doc changes needed for
https://microsoft.github.io/autogen/. See
https://microsoft.github.io/autogen/docs/Contribute#documentation to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

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## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
solves. -->
Typo in example text

## Related issue number

<!-- For example: "Closes #1234" -->

## Checks

- [X] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [X] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [X] I've made sure all auto checks have passed.

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## Why are these changes needed?

- Running a team generates a MemoryQueryEvent has a MemoryContent field
and is part of the model context
- Saving team state (`team.save_state()`) includes serializing model
context
- MemoryContent has a mime_type field, which was not being properly
serialized.
"Object of type MemoryMimeType is not JSON serializable"

This PR? -> add explicit serialization instruction for the mimetype
field.

```python
import asyncio
import logging
import json
import yaml, aiofiles
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_core.memory import ListMemory, MemoryContent, MemoryMimeType
from autogen_agentchat.ui import Console

logger = logging.getLogger(__name__)

state_path = "team_state.json"

model_client =  OpenAIChatCompletionClient(model="gpt-4o-mini")

max_msg_termination = MaxMessageTermination(max_messages=3)
# Initialize user memory
user_memory = ListMemory()

# Add user preferences to memory
await user_memory.add(MemoryContent(content="The weather should be in metric units", mime_type=MemoryMimeType.TEXT))

# Create the team.
agent = AssistantAgent(
    name="assistant",
    model_client=model_client,
    system_message="You are a helpful assistant.",
    memory = [user_memory],
)
yoda = AssistantAgent(
    name="yoda",
    model_client=model_client,
    system_message="Repeat the same message in the tone of Yoda.",
)

team = RoundRobinGroupChat(
    [agent, yoda],
    termination_condition=max_msg_termination
)

await Console(team.run_stream(task="Hi, How are you ?"))
 
# Save team state to file.
state = await team.save_state()
with open(state_path, "w") as f:
    json.dump(state, f)
# Load team state from file.
with open("team_state.json", "r") as f:
    team_state = json.load(f)
    
await team.load_state(state) 
await Console( team.run_stream(task="What was the last thing that was said in this conversation "))
```

<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

<!-- For example: "Closes #1234" -->

Closes #5688
## Checks

- [ ] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.

Closes #4904 

Does not change default behavior in core.

In agentchat, this change will mean that exceptions that used to be
ignored and result in bugs like the group chat stopping are now reported
out to the user application.

---------

Co-authored-by: Ben Constable <benconstable@microsoft.com>
Co-authored-by: Ryan Sweet <rysweet@microsoft.com>

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assign them to your PR. -->

## Why are these changes needed?

Fixes visual accessibility issues `(6) Luminosity contrast ratio of
focus indicator on 'Venv' control is less than 3:1.` and `(20) "Only
color is used to indicate the focus indicator on the 'Venv', and 'conda'
Tab controls."`

## Related issue number

#5633 

before:
<img width="162" alt="image"
src="https://github.com/user-attachments/assets/ac317e82-c82e-4d3b-910b-e9eb5d087340"
/>

![image](https://github.com/user-attachments/assets/2971043a-8b5b-4521-8135-c411c0ecab1f)

after:
<img width="160" alt="image"
src="https://github.com/user-attachments/assets/8053ae77-4d62-491b-9d78-657646ed71a6"
/>

![image](https://github.com/user-attachments/assets/5c3ac2c3-4813-461b-8958-4665860574a0)

<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->

Claude 3.7 just came out. Its a pretty capable model and it would be
great to support it in Autogen.
This will could augment the already excellent support we have for
Anthropic via the SKAdapters in the following ways

- Based on the ChatCompletion API similar to the ollama and openai
client
- Configurable/serializable (can be dumped) .. this means it can be used
easily in AGS.

## What is Supported 

(video below shows the client being used in autogen studio)

https://github.com/user-attachments/assets/8fb7c17c-9f9c-4525-aa9c-f256aad0f40b



- streaming 
- tool callign / function calling 
- drop in integration with assistant agent. 
- multimodal support

```python

from dotenv import load_dotenv
import os 

load_dotenv()

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.models.anthropic import AnthropicChatCompletionClient 
model_client =   AnthropicChatCompletionClient(
        model="claude-3-7-sonnet-20250219" 
    )

async def get_weather(city: str) -> str:
    """Get the weather for a given city."""
    return f"The weather in {city} is 73 degrees and Sunny."

 
agent = AssistantAgent(
    name="weather_agent",
    model_client=model_client,
    tools=[get_weather],
    system_message="You are a helpful assistant.", 
    # model_client_stream=True,   
)

# Run the agent and stream the messages to the console.
async def main() -> None:
    await Console(agent.run_stream(task="What is the weather in New York?"))
await main()
```

result 

```
messages = [
    UserMessage(content="Write a very short story about a dragon.", source="user"),
]

# Create a stream.
stream = model_client.create_stream(messages=messages)

# Iterate over the stream and print the responses.
print("Streamed responses:")
async for response in stream:  # type: ignore
    if isinstance(response, str):
        # A partial response is a string.
        print(response, flush=True, end="")
    else:
        # The last response is a CreateResult object with the complete message.
        print("\n\n------------\n")
        print("The complete response:", flush=True)
        print(response.content, flush=True)
        print("\n\n------------\n")
        print("The token usage was:", flush=True)
        print(response.usage, flush=True)
```

 

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assign them to your PR. -->

## Why are these changes needed?

<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issue number

<!-- For example: "Closes #1234" --> 

Closes #5205 
Closes #5708

## Checks

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<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed. 



cc @rohanthacker

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pull request. -->

update human in the loop docs for agentchat  
..

> Outputs in docs are outdated as summary is not printed out by default
when using Console
[#5590](https://github.com/microsoft/autogen/issues/5590)

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## Why are these changes needed?

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## Related issue number

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Closes #5590

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## Why are these changes needed?

Fixes `(14) Ensures the contrast between foreground and background
colors meets WCAG 2 AA minimum contrast ratio thresholds`

Note: the color values don't output at the exact values on the
stylesheet. For example, the value `#1774E5` evaluates to `#2274E0` by
the Accessibility Insights app.

## Related issue number

https://github.com/microsoft/autogen/issues/5633

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This PR has 3 main improvements. 

- Token streaming 
- Adds support for environment variables in the app settings 
- Updates AGS to persist Gallery entry in db.

## Adds Token Streaming in AGS.  

Agentchat now supports streaming of tokens via
`ModelClientStreamingChunkEvent `. This PR is to track progress on
supporting that in the AutoGen Studio UI.

If `model_client_stream` is enabled in an assitant agent, then token
will be streamed in UI.

```python
streaming_assistant = AssistantAgent(
    name="assistant",
    model_client=model_client,
    system_message="You are a helpful assistant.",
    model_client_stream=True,  # Enable streaming tokens.
)

```

https://github.com/user-attachments/assets/74d43d78-6359-40c3-a78e-c84dcb5e02a1


## Env Variables 
Also adds support for env variables in AGS Settings

You can set env variables that are loaded just before a team is run.
Handy to set variable to be used by tools etc.

<img width="1291" alt="image"
src="https://github.com/user-attachments/assets/437b9d90-ccee-42f7-be5d-94ab191afd67"
/>


> Note: the set variables are available to the server process.

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## Why are these changes needed?

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## Related issue number

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Closes #5627  
Closes #5662 
Closes #5619 

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Resolves #5408

Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>

Resolves #5610

And address various questions regarding to how to use user proxy agent
and human-in-the-loop.

Replace the undefined `tools` variable with `tool_schema` parameter

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## Why are these changes needed?
This change keeps the documentation up to date :
https://microsoft.github.io/autogen/stable//user-guide/core-user-guide/components/tools.html

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Resolves #5640

## Why are these changes needed?

Fix minor issues in docs:
- probably editing left over
- typo
- code formatting inconsistency

## Related issue number

N/A

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Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>

Updates:
- added missing backtick for formatting class name
- typo in user response example

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

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This PR makes makes ChatCompletionCache   support component config

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## Why are these changes needed? 

Ensures we have a path to serializing ChatCompletionCache , similar to
the ChatCompletion client that it wraps.

This PR does the following

- Makes CacheStore serializable first (part of this includes converting
from Protocol to base class). Makes it's derivatives serializable as
well (diskcache, redis)
- Makes ChatCompletionCache serializable 
- Adds some tests

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Closes #5141

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cc @nour-bouzid

Resolves #5192

Test

```python
import asyncio
import os
from random import randint
from typing import List
from autogen_core.tools import BaseTool, FunctionTool
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console

async def get_current_time(city: str) -> str:
    return f"The current time in {city} is {randint(0, 23)}:{randint(0, 59)}."

tools: List[BaseTool] = [
    FunctionTool(
        get_current_time,
        name="get_current_time",
        description="Get current time for a city.",
    ),
]

model_client = OpenAIChatCompletionClient(
    model="anthropic/claude-3.5-haiku-20241022",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
    model_info={
        "family": "claude-3.5-haiku",
        "function_calling": True,
        "vision": False,
        "json_output": False,
    }
)

agent = AssistantAgent(
    name="Agent",
    model_client=model_client,
    tools=tools,
    system_message= "You are an assistant with some tools that can be used to answer some questions",
)

async def main() -> None:
    await Console(agent.run_stream(task="What is current time of Paris and Toronto?"))

asyncio.run(main())
```

```
---------- user ----------
What is current time of Paris and Toronto?
---------- Agent ----------
I'll help you find the current time for Paris and Toronto by using the get_current_time function for each city.
---------- Agent ----------
[FunctionCall(id='toolu_01NwP3fNAwcYKn1x656Dq9xW', arguments='{"city": "Paris"}', name='get_current_time'), FunctionCall(id='toolu_018d4cWSy3TxXhjgmLYFrfRt', arguments='{"city": "Toronto"}', name='get_current_time')]
---------- Agent ----------
[FunctionExecutionResult(content='The current time in Paris is 1:10.', call_id='toolu_01NwP3fNAwcYKn1x656Dq9xW', is_error=False), FunctionExecutionResult(content='The current time in Toronto is 7:28.', call_id='toolu_018d4cWSy3TxXhjgmLYFrfRt', is_error=False)]
---------- Agent ----------
The current time in Paris is 1:10.
The current time in Toronto is 7:28.
```

---------

Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>

Fix typo in doc

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Fixing grammar issues

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## Why are these changes needed?

Adds ollama client documentation to the docs page

## Related issue number

https://github.com/microsoft/autogen/issues/5604

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## Why are these changes needed?

Just fixing a very minor typo I noticed while reading the docs.

`recpients` -> `recipients`

Resolves #5519

Also spotted and fixed a bug that caused value error from `register_factory`, when the `expected_class` was not provided.

Introduce validation for the ModelInfo dictionary to ensure required
fields are present.

Resolves #5501

- Add a section on how to use model client with tools
- Replace the obsecure pattern of using ToolAgent with a simpler
implementation of calling tool within the agent itself.

Resolves #5508

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## Why are these changes needed?

Make
[PythonCodeExecutionTool](https://github.com/microsoft/autogen/blob/main/python/packages/autogen-ext/src/autogen_ext/tools/code_execution/_code_execution.py)
declarative so it can be used in tools like AGS

Summary of changes

- Make CodeExecutor declarative (convert from Protocol to ABC, inherit
from ComponentBase)
- Make LocalCommandLineCodeExecutor, JupyterCodeExecutor and
DockerCommandLineCodeExecutor declarative , best effort. Not all fields
are serialized, warnings are shown where appropriate.
- Make PythonCodeExecutionTool declarative.

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## Related issue number

Closes #5526 
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Pulls the .NET Docs a bit more into the documentation website proper:

* Add a link from the 0.4-series Python docs to 
* Adds a transient redirect from /dotnet/ to /dotnet/dev/ to forward to
the dev docs until we do a non "-dev" release