Shane McAllister

Lead Developer Advocacy (Global)

> All of the features of AWS CodeWhisperer have moved to Amazon Q Developer. You can see the updated post about how to use Amazon Q [HERE][1]

Amazon CodeWhisperer is trained on billions of lines of code and can generate code suggestions — ranging from snippets to full functions — in real-time, based on your comments and existing code. AI code assistants have revolutionized developers’ coding experience, but what sets Amazon CodeWhisperer apart is that MongoDB has collaborated with the AWS Data Science team, enhancing its capabilities!

:youtube[]{vid=euVzChXRO9w}

At MongoDB, we are always looking to enhance the developer experience, and we've fine-tuned the AWS CodeWhisperer Foundational Models to deliver top-notch code suggestions — trained on, and tailored for, MongoDB. This gives developers of all levels the best possible experience when using AWS CodeWhisperer for MongoDB functions. 

This tutorial will help you get AWS CodeWhisperer up and running in VS Code, but CodeWhisperer also works with a number of other IDEs, including IntelliJ IDEA, AWS Cloud9, AWS Lambda console, JupyterLab, and Amazon SageMaker Studio. On the [Amazon CodeWhisperer site][2], you can find tutorials that demonstrate how to set up AWS CodeWhisperer on different IDEs, as well as other documentation.

*Note:* Amazon CodeWhisperer allows users to start without an AWS account because usually, creating an AWS account requires a credit card. Currently, CodeWhisperer is free for individual users. So it’s super easy to get up and running.

**Installing Amazon CodeWhisperer for VS Code** 

CodeWhisperer doesn’t have its own VS Code extension. It is part of a larger extension for AWS services called AWS Toolkit. AWS Toolkit is available in the VS Code extensions store. 

 1. Open VS Code and navigate to the extensions store (bottom icon on the left panel).
 2. Search for AWS CodeWhisperer and it will show up as part of the AWS Toolkit.
![Searching for the AWS ToolKit Extension][3]
 3. Once found, hit Install. Next, you’ll see the full AWS Toolkit
    Listing
![The AWS Toolkit full listing][4]
 4. Once installed, you’ll need to authorize Amazon CodeWhisperer via a Builder
    ID to connect to your AWS developer account (or set up a new account
    if you don’t already have one).
![Authorise AWS CodeWhisperer][5]

**Using AWS CodeWhisperer**

Navigating code suggestions 

![AWS CodeWhisperer Running][6]

With Amazon CodeWhisperer installed and running, as you enter your prompt or code, CodeWhisperer will offer inline code suggestions. If you want to keep the suggestion, use **TAB** to accept it. AWS CodeWhisperer may provide multiple suggestions to choose from depending on your use case. To navigate between suggestions, use the left and right arrow keys to view them, and **TAB** to accept.

If you don’t like the suggestions you see, keep typing (or hit **ESC**). The suggestions will disappear, and Amazon CodeWhisperer will generate new ones at a later point based on the additional context.

**Requesting suggestions manually**

You can request suggestions at any time. Use **Option-C** on Mac or **ALT-C** on Windows. After you receive suggestions, use **TAB** to accept and arrow keys to navigate.

**Getting the best recommendations**

For best results, follow these practices.

 - Give Amazon CodeWhisperer something to work with. The more code your file contains, the more context CodeWhisperer has for generating recommendations.
 - Write descriptive comments in natural language — for example
```
// Take a JSON document as a String and store it in MongoDB returning the _id
```
Or
```
//Insert a document in a collection with a given _id and a discountLevel
```
 - Specify the libraries you prefer at the start of your file by using import statements.
```
// This Java class works with MongoDB sync driver.
// This class implements Connection to MongoDB and CRUD methods.
```
 - Use descriptive names for variables and functions
 - Break down complex tasks into simpler tasks

**Provide feedback**

As with all generative AI tools, they are forever learning and forever expanding their foundational knowledge base, and MongoDB is looking for feedback. If you are using Amazon CodeWhisperer in your MongoDB development, we’d love to hear from you. 

We’ve created a special “codewhisperer” tag on our [Developer Forums][7], and if you tag any post with this, it will be visible to our Amazon CodeWhisperer project team and we will get right on it to help and provide feedback. If you want to see what others are doing with CodeWhisperer on our forums, the [tag search link][8] will jump you straight into all the action. 

We can’t wait to see your thoughts and impressions of MongoDB and Amazon CodeWhisperer together. 


  [1]: https://mongodb.prakticum-team.ru/developer/products/mongodb/getting-started-with-mongodb-and-amazon-q-developer/
  [2]: https://aws.amazon.com/codewhisperer/resources/#Getting_started
  [3]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt1bfd28a846063ae9/65481ef6e965d6040a3dcc37/CW_1.png
  [4]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/bltde40d5ae1b9dd8dd/65481ef615630d040a4b2588/CW_2.png
  [5]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt636bb8d307bebcee/65481ef6a6e009040a740b86/CW_3.png
  [6]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/bltf1e0ebeea2089e6a/65481ef6077aca040a5349da/CW_4.png
  [7]: https://mongodb.prakticum-team.ru/community/forums/
  [8]: https://mongodb.prakticum-team.ru/community/forums/tag/codewhisperer

Get AWS CodeWhisperer running in VS Code. It also works with IDEs like IntelliJ IDEA, AWS Cloud9, AWS Lambda console, JupyterLab, & SageMaker Studio.

Getting Started With MongoDB and AWS CodeWhisperer

In 2023, MongoDB collaborated with the Amazon CodeWhisperer team to fine tune the Foundational Models to deliver top-notch code suggestions — trained on, and tailored for, MongoDB. Now, we've taken this collaboration even further with Amazon Q Developer, the latest evolution of Amazon’s AI-powered coding assistant, to provide even better intelligent code completions and enhanced productivity for your MongoDB-based projects. 

Amazon Q Developer enhances the coding experience, providing faster and more context-aware code completions, which seamlessly integrate with MongoDB operations. Its AI capabilities have been expanded, offering better support for complex tasks and workflows and with features like Q Chat, you can even start a conversation with Amazon Q to explore and explain code suggestions further. Check out this short video for an idea of the capabilities of Amazon Q with MongoDB.

:youtube[]{vid=oFTC32XZZ4w}

This tutorial below will help you get Amazon Q Developer up and running in VS Code, but Q is also available for a number of other IDEs, including including JetBrains (as in the video above), IntelliJ IDEA, Visual Studio as well as the CLI and the AWS Console. On the [Amazon Q Site][2], you can find tutorials that demonstrate how to set up Amazon Q on different IDEs, as well as other documentation.

**Installing Amazon Q for VS Code** 

Amazon Q is available in the VS Code extensions store. 

 1. Open VS Code and navigate to the extensions store (bottom icon on the left panel).
 2. Search for Amazon Q and it will show up in the extension marketplace.
![AmazonQ extension][3]
 3. Once found, hit Install. 
![Amazonq details][4]
 4. Once installed, you’ll be prompted to authorize Amazon Q via a Builder
    ID to connect to your AWS developer account (or set up a new account
    if you don’t already have one).
![enter image description here][5]

**Using Amazon Q**

The are 2 ways you can interact with Amazon Q - Inline Code Suggestions or Q Chat

**Inline code suggestions**
 
![amazon q running][6]

With Amazon Q Developer installed and running, as you enter your prompt or code, Q will offer inline code suggestions. If you want to keep the suggestion, use **TAB** to accept it. Q may provide multiple suggestions to choose from depending on your use case. To navigate between suggestions, use the left and right arrow keys to view them, and **TAB** to accept.

If you don’t like the suggestions you see, keep typing (or hit **ESC**). The suggestions will disappear, and Q will generate new ones at a later point based on the additional context.

**Requesting suggestions manually**

You can request suggestions at any time. Use **Option-C** on Mac or **ALT-C** on Windows. After you receive suggestions, use **TAB** to accept and arrow keys to navigate.

**Getting the best recommendations**

For best results, follow these practices.

 - Give Amazon Q something to work with. The more code your file contains, the more context Q has for generating recommendations.
 - Write descriptive comments in natural language — for example
```
// Take a JSON document as a String and store it in MongoDB returning the _id
```
Or
```
//Insert a document in a collection with a given _id and a discountLevel
```
 - Specify the libraries you prefer at the start of your file by using import statements.
```
// This Java class works with MongoDB sync driver.
// This class implements Connection to MongoDB and CRUD methods.
```
 - Use descriptive names for variables and functions
 - Break down complex tasks into simpler tasks

**Using Q Chat**

If you prefer a Chat like interface, Q Chat is here to help also and you can interact in a more conversation way, and all the while, being able to insert code suggestions back into your project. The choice is yours, inline or chat, you'll get same capability and assistance, all underpinned by MongoDB training and fine tuning. 

![amazon q chat][7]

**Provide feedback**

As with all generative AI tools, they are forever learning and forever expanding their foundational knowledge base, and MongoDB is looking for feedback. If you are using Amazon Q in your MongoDB development, we’d love to hear from you, so please visit [our Forums][8] to let us know how you're getting on with it, and how it's helping in your workflow. 

We can’t wait to see your thoughts and impressions of MongoDB and Amazon Q together. 


  [1]: https://youtu.be/oFTC32XZZ4w
  [2]: https://aws.amazon.com/q/getting-started/
  [3]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt1664d1250cd1604a/66f440ab5d63981560237b5a/Screenshot_2024-09-25_at_17.32.06.png
  [4]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blte973b291c66ecb58/66f440ce1c93ab8c48d54ffd/Screenshot_2024-09-25_at_17.33.32.png
  [5]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt3983a331c916642e/66f440ecd9d2f856d5219c9f/Screenshot_2024-09-25_at_17.36.53.png
  [6]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt729bafccc8e543e2/66f4411b848c0a4871617809/Screenshot_2024-09-25_at_17.38.06.png
  [7]: https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt4e643473bef6493e/66f4414a5ff09a7e2d1cfe5c/Screenshot_2024-09-25_at_17.37.52.png
  [8]: https://mongodb.prakticum-team.ru/community/forums/

Get working on your MongoDB Project swiftly with Amazon Q Developer

Getting Started With MongoDB and Amazon Q Developer generative AI–powered coding assistant

> Some features mentioned below will be deprecated on Sep. 30, 2025. [Learn more](https://mongodb.prakticum-team.ru/products/updates/product-support-deprecation?tck=notice_product_deprecation_2024). 

Leafsteroids is a MongoDB Demo showing the following services and integrations
------------------------------------------------------------------------
![Leafsteroids Architecture](https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt26770d2ea3d3004a/649227e9d40ad065f56824c2/Screenshot_2023-06-19_at_18.14.15.png)

**Atlas App Services**  
All in one backend. Atlas App Services offers a full-blown REST service using Atlas Functions and HTTPS endpoints.  

**Atlas Search**  
Used to find the player nickname in the Web UI. 

**Atlas Charts**  
Event & personalized player dashboards accessible over the web. Built-in visualization right with your data. No additional tools required. 

**Document Model**  
Every game run is a single document demonstrating rich documents and “data that works together lives together”, while other data entities are simple collections (configuration). 

**AWS Beanstalk** Hosts the Blazor Server Application (website). 

**AWS EC2**  
Used internally by AWS Beanstalk. Used to host our Python game server. 

**AWS S3** 
Used internally by AWS Beanstalk. 

**AWS Private Cloud**  
Private VPN connection between AWS and MongoDB. 

![leafsteroids_demo](https://images.contentstack.io/v3/assets/blt39790b633ee0d5a7/blt321f845875f27d15/6491d63975c2ae28f20612e5/leafsteroids_AdobeExpress.gif)  

**At a MongoDB .local Event and want to register to play Leafsteroids? [Register Here](https://github.com/mongodb-developer/leafsteroids)**

You can build & play Leafsteroids yourself with the following links

## Development Resources 
|Resource|	Link|
|---|---|
|Github Repo	|[Here](https://github.com/mongodb-developer/leafsteroids)|
|MongoDB TV Livestream	|[Here](https://mongodb.prakticum-team.ru/developer/videos/aws-cloud-connect-leafsteroids/)|
|MongoDB & AWS	|[Here](https://mongodb.prakticum-team.ru/mongodb-on-aws)|
|MongoDB on the AWS Marketplace	|[Here](https://aws.amazon.com/marketplace/pp/prodview-pp445qepfdy34)|


Shane McAllister

Content by Shane McAllister

Getting Started With MongoDB and AWS CodeWhisperer

Getting Started With MongoDB and Amazon Q Developer generative AI–powered coding assistant

Leafsteroid Resources