Add video to explain the deploy from Codespaces (#361)

## Purpose

Add a video to describe and explain the process to deploy to Azure from
a Codespace.

## Does this introduce a breaking change?
<!-- Mark one with an "x". -->
```
[ ] Yes
[X] No
```

## Pull Request Type
What kind of change does this Pull Request introduce?

<!-- Please check the one that applies to this PR using "x". -->
```
[ ] Bugfix
[ ] Feature
[ ] Code style update (formatting, local variables)
[ ] Refactoring (no functional changes, no api changes)
[X] Documentation content changes
[ ] Other... Please describe:
```

## How to Test
*  Get the code

```
git clone [repo-address]
cd [repo-name]
git checkout [branch-name]
```

* Test the code

In bash or a console
```
azd up
```

README.md

@@ -142,7 +142,8 @@ Then, run the following commands to get the project on your local environment:
 
 #### Deploying from scratch
 
-[📺 Live stream: Deploying from scratch](https://youtu.be/wgSnkxGH2Sk?si=C4zAbLKhK3LoAS43)
+[📺 Live stream: Deploying from scratch in CodeSpaces](https://youtu.be/TORUsRNimM0)
+[📺 Live stream: Deploying from scratch in Windows 11](https://youtu.be/wgSnkxGH2Sk?si=C4zAbLKhK3LoAS43)
 
 > **Important**:
 > Ensure Docker is running before running any `azd` provisioning / deployment commands.
@@ -380,4 +381,4 @@ To reduce costs, you can switch to free SKUs for various services, but those SKU
 
 **_Question_**: Why do we need to break up the PDFs into chunks when Azure AI Search supports searching large documents?
 
-**_Answer_**: Chunking allows us to limit the amount of information we send to OpenAI due to token limits. By breaking up the content, it allows us to easily find potential chunks of text that we can inject into OpenAI. The method of chunking we use leverages a sliding window of text such that sentences that end one chunk will start the next. This allows us to reduce the chance of losing the context of the text.
+**_Answer_**: Chunking allows us to limit the amount of information we send to OpenAI due to token limits. By breaking up the content, it allows us to easily find potential chunks of text that we can inject into OpenAI. The method of chunking we use leverages a sliding window of text such that sentences that end one chunk will start the next. This allows us to reduce the chance of losing the context of the text.