Tag Archives: Amazon Q

Effortlessly execute AWS CLI commands using natural language with Amazon Q Developer

Post Syndicated from Xipu Li original https://aws.amazon.com/blogs/devops/effortlessly-execute-aws-cli-commands-using-natural-language-with-amazon-q-developer/

The CLI struggle is real

Command-line tools are meant to simplify infrastructure and DevOps workflows, but the reality is often the opposite. Instead of speeding things up, the vast array of commands, flags, and syntax turns the CLI into a puzzle. Tools meant to enhance productivity have developers endlessly tab-switching between searches, forums, and docs just to find basic commands.

Much like learning a new language, mastering the CLI involves navigating intricate and unfamiliar syntax. In the real world, we have translators to help bridge communication gaps—and now, there’s Amazon Q Developer to do the same for your CLI.

In this post, we’ll guide you through setting up Amazon Q Developer on your command line and demonstrate how it simplifies complex tasks.

Getting started

Make sure you have AWS CLI installed and configured to have a smooth experience as you go through this post.

To use Amazon Q Developer in your CLI, start by installing Amazon Q. Currently, Amazon Q’s CLI capability is only available for macOS and Linux users, so make sure you are on the right platform.

Using Amazon Q Developer requires you to have an AWS Builder ID or be part of an organization with an AWS IAM Identity Center instance set up.

We will be interacting with Amazon S3 and Amazon CloudFront, check that your AWS account has the necessary permissions:

s3:CreateBucket 
s3:PutObject 
s3:PutBucketPolicy 
cloudfront:CreateDistribution 
cloudfront:CreateOriginAccessControl 
cloudfront:GetDistributionConfig 
cloudfront:UpdateDistribution

Once you have these prerequisites in place, you’re ready to see how Amazon Q Developer can simplify your CLI workflow. Let’s get started.

Transforming your CLI experience

Open a new terminal window, and type q translate, followed by a natural language prompt to execute a complex command. Amazon Q Developer will take your input and process it through a Large Language Model (LLM), generating the corresponding bash command.

Let’s start with something simple and fun: count down from 10 second by second in your terminal:

q translate “Count down from 10 second by second”

When you execute the command, you’ll see Amazon Q suggest an executable shell command with a few action items:

Screenshot of terminal executing the command

  • Execute command: executes the proposed command immediately
  • Edit command: allows you to modify the proposed command
  • Regenerate answer: proposes another command
  • Ask another question: allows you to enter a new prompt
  • Cancel: exits the session

For now, we can just execute the command and see it in action:

Building a website with AWS from the CLI

Now that we’ve covered the basics, let’s get our hands dirty with something practical. In this tutorial, we’ll walk through creating a static website hosted on AWS, entirely using the command line.

Step 1: Create an S3 Bucket to Host the Website

Amazon S3 is great for static website hosting. First, we need to create an S3 bucket to store our website files. We can simply instruct Amazon Q Developer to “create an S3 bucket called ‘ai-generated-bucket’ on us-west-2”:

Screenshot of terminal executing the command

Note: that if you already have a bucket with the same name, feel free to change it to something else.

We can see that Amazon Q Developer has correctly generated the command:

 aws s3 mb s3://ai-generated-bucket —region us-west-2

If you see something that’s not quite right, you can try to “Edit command” or “Regenerate answer”. For now, we can just execute that generated command and verify in the AWS S3 console that the S3 bucket has indeed been created.

Step 2: Create an HTML File with “Hello World”

Now, we need to upload a basic HTML file to the bucket. Instead of manually writing it, we can let Amazon Q Developer guide us. Prompt:

q translate "Create a simple index.html file with 'Hello World' message"

Screenshot of terminal executing the command

Amazon Q Developer is suggesting echo '<h1>Hello World</h1>' > index.html, which looks good. We can go ahead execute that command.

Step 3: Upload the HTML File to S3

Now, we’ll upload the index.html file to the S3 bucket:

q translate "Upload index.html to the 'ai-generated-bucket' S3 bucket"

Screenshot of terminal executing the command

After executing the command, we can check on the Amazon S3 console to see we have successfully uploaded the index.html file to our bucket.

Screenshot of Amazon S3 console

Step 4: Set up CloudFront distribution

By default, Amazon S3 blocks public access to your account and buckets. We recommend keeping Block Public Access enabled for production use cases and securely serve your site through Amazon CloudFront, a global Content Delivery Network (CDN) that securely delivers your content with low latency and high transfer speeds, making it ideal for hosting static websites.

Use Amazon Q Developer to create a CloudFront distribution for your S3 bucket:

q translate "Create a CloudFront distribution for my S3 bucket 'ai-generated-bucket'"

Screenshot of terminal executing the command
Hmm, that doesn’t look right. Amazon Q Developer is trying to supply —distribution-config with an unspecified configuration file. To fix this, you can either manually provide the CloudFront distribution configuration or we can push the limit of what Amazon Q Developer can do.

Let’s try to refine our prompt so that we don’t use the --distribution-config flag:

q translate "Create a CloudFront distribution for my S3 bucket 'ai-generated-bucket' without using the ‘—distribution-config' flag"

Screenshot of terminal executing the command

Much better! Looks like we need to replace the ‘X’s with an actual origin domain name. In our case, the S3 bucket origin uses the following format:

bucket-name.s3.amazonaws.com

Navigate to and toggle the Edit command option on your CLI, and replace the ‘X’s. In our case, the replacement value would be ai-generated-bucket.s3.amazonaws.com. Execute the command, and upon success, you should be able to see the CloudFront configuration printed on the console:

Screenshot of terminal printing out the CloudFront configuration

Copy the “Id” from the output, we will need it for the next step.

Step 5: Preparing for public access

We want to set index.html as our default root object for the CloudFront distribution we just created, this will ensure that when users access your website’s root URL, CloudFront automatically serves the index.html file without requiring the user to explicitly specify it in the URL.

q translate "Update my CloudFront distribution CLOUDFRONT_ID to have 'index.html' as default root object without using the --distribution-config flag"

Screenshot of terminal executing the command
Don’t forget to replace CLOUDFRONT_ID with the Id you retrieved from the previous step. Once you execute that command, you should see the CloudFront configuration printed on the console like the step above.

Now, to securely serve our HTML file to the public, we need to perform the following:

  1. set up an Origin Access Control (OAC)
  2. attach that OAC to our CloudFront distribution
  3. update our S3 bucket policy

Let’s have Amazon Q generate an OAC for us:

q translate "Create an OAC named 'ai-generated-oac' for my CloudFront distribution CLOUDFRONT_ID"

Screenshot of terminal executing the command
When we execute the command, we get an error saying:

An error occurred (MalformedXML) when calling the CreateOriginAccessControl operation: 1 validation error detected: Value 'origin-access-identity' at 'originAccessControlConfig.originAccessControlOriginType' failed to satisfy constraint: Member must satisfy enum value set: [s3, lambda, mediastore, mediapackagev2]

It might be difficult to debug this error message without consulting the internet. Luckily, Amazon Q Developer offers another powerful capability, q chat, which is like a chatbot in your terminal. Let’s try it out to troubleshoot:

q chat "@history An error occurred (MalformedXML) when calling the CreateOriginAccessControl operation: 1 validation error detected: Value 'origin-access-identity' at 'originAccessControlConfig.originAccessControlOriginType' failed to satisfy constraint: Member must satisfy enum value set: [s3, lambda, mediastore, mediapackagev2]. Help me resolve this error"

Note that I included @history in the prompt to pass our shell history to Amazon Q so it can respond based on the context provided.

Screenshot of terminal executing the command

We get a detailed AI generated response on our terminal with step by step instructions and citations! By using the @history tag, Amazon Q Developer knows to relate the error message to the correct shell command we executed (we can see that it knows we want to create an OAC with name ai-generated-oac without us explicitly telling it so).

Let’s try executing the revised bash command:

aws cloudfront create-origin-access-control \
    --origin-access-control-config \
    Name=ai-generated-oac,\
    OriginAccessControlOriginType=s3,\
    SigningBehavior=always,\
    SigningProtocol=sigv4
This time, it succeeded:
Screenshot of terminal printing the OAC response

Take a note of the Id field, we will need it later.

We can now ask q chat about how to attach the generated OAC to our CloudFront distribution:

q chat "@history Update my CloudFront distribution CLOUDFRONT_ID with the OAC we just generated, be specific"

Screenshot of terminal executing the command

Notice how @history even helps Amazon Q fill in CLOUDFRONT_ID for me!

We just need to follow the instruction and once we completed the last step, we should see an updated CloudFront distribution in our console output:

Screenshot of terminal printing the updated CloudFront distribution
It shows a “Status” of “InProgress”. To proceed to the next step, we will need to wait for CloudFront to finish deployment. Let’s ask Amazon Q Developer how to get the latest status:

q translate "Get the status of my CloudFront distribution with CLOUDFRONT_ID on us-west-2"

Screenshot of terminal executing the command

Replace $CLOUDFRONT_ID with your own and execute the command to get the latest status. It might take a few minutes, it’s time to grab a coffee or go for a short walk!

Once the status is Deployed, we can go ahead and update our S3 bucket policy to give public read access to our CloudFront distribution:

Screenshot of terminal executing the command

You can see we are using a very vague prompt but @history made sure that Amazon Q has enough context to generate helpful responses.

Simply follow the instruction and we’re ready to access the site!

Step 6: Access the site 🎉

Let’s get the public URL of our CloudFront distribution.

Screenshot of terminal executing the command

Once you get the URL, you can open it in a browser, and it should show “Hello World”!

Screenshot of web page displaying “Hello World”

Conclusion

In this post, we’ve successfully:

  1. Created an S3 bucket to host our website files
  2. Generated a simple HTML file with a “Hello World” message
  3. Uploaded the HTML file to our S3 bucket
  4. Created a CloudFront distribution to serve our website securely
  5. Set up an Origin Access Control (OAC) for our CloudFront distribution
  6. Updated our S3 bucket policy to allow access from CloudFront
  7. Accessed our website through the CloudFront URL

All of these steps were accomplished on our terminal using Amazon Q Developer on the CLI.

You’ve seen how effortlessly complex CLI commands can be translated into natural language, making AWS more accessible than ever. But this is just the start. As your needs grow, Amazon Q scales with you, delivering faster deployments, enhanced productivity, and seamless access to the full AWS ecosystem.

We encourage you to explore AWS services you haven’t yet tried, with Amazon Q Developer simplifying the process every step of the way. Let it streamline your workflow and unlock new opportunities to innovate and grow.

Unlocking AWS Console: Diagnosing Errors with Amazon Q Developer

Post Syndicated from Marco Frattallone original https://aws.amazon.com/blogs/devops/unlocking-aws-console-diagnosing-errors-with-amazon-q-developer/

Introduction

Developers, IT Operators, and in some cases, Site Reliability Engineers (SREs) are responsible for deploying and operating infrastructure and applications, as well as responding to and resolving incidents effectively and in a timely manner. Effective incident management requires quick diagnosis, root cause analysis, and implementation of corrective actions. Diagnosing the root cause can be challenging in the context of modern systems that involve multiple resources deployed across distributed environments. Amazon Q Developer, a generative AI-powered assistant, can help simplify this process by diagnosing errors you receive in the AWS Management Console.

Amazon Q Developer can save you critical time when dealing with production issues by helping to diagnose errors related to your AWS environment. These errors could be the result of potential misconfiguration across multiple resources, and usually requires you to navigate between several service consoles to identify the root cause. Amazon Q Developer applies machine learning models to automate diagnosis of errors that arise in the AWS Console interface. This reduces the mean time to repair (MTTR) and minimizes the impact of incidents on business operations.

This blog post explores the Amazon Q Developer feature to diagnose errors in AWS Console while working with AWS services. We describe how this feature works in order to provide you guidance on troubleshooting. We take a look behind-the-scenes to show the processes that power this feature.

Diagnose with Amazon Q

The Diagnose with Amazon Q feature is activated when an error occurs in the console for an AWS service that is currently supported by this functionality, and a user with appropriate permissions clicks the Diagnose with Amazon Q button next to the error message. Amazon Q provides a natural language explanation that analyzes the root cause of the error. With a second click on Help me resolve, Amazon Q displays an ordered list of instructions which can be used to resolve the error condition. Once completed, you can provide feedback on whether the resolution provided by Amazon Q was helpful.

To make things concrete, we consider two running examples.

Example 1: Assume that you try to delete an S3 bucket which is not empty. This results in an error message:

This bucket is not empty. Buckets must be empty before they can be deleted. To
delete all objects in the bucket, use the empty bucket configuration.

Example 2: Suppose that you try to list objects in a particular S3 bucket, but lack IAM permissions to do so. This results in an error message:

Insufficient permissions to list objects. After you or your AWS administrator has updated your permissions to allow the s3:ListBucketaction, refresh the page. Learn more about Identity and access management in
Amazon S3.

User clicks on “Launch Instances” button In the EC2 service console in the AWS Management console. User enters all the required information, and clicks on “Launch Instance” button. This results in “Instance launch failed” error appearing in the console along with a “Diagnose with Amazon Q” button. User clicks on the button. this brings up a new window titled “Diagnose console errors with Amazon Q”. Soon an “Analysis” section appears with the message describing the issue with IAM permissions to launch new EC2 instances using natural language. User clicks on “Help me resolve” button. After few seconds, “Resolution” section along with the steps to resolve the error appears.

Diagnose with Amazon Q IAM permissions related to EC2 instance launch error

Behind the Scenes: How Amazon Q generates a diagnosis

When you click on Diagnose with Amazon Q button next to the error message in the AWS Management Console, Amazon Q generates an Analysis that expresses the root cause of the error in natural language. This step is assisted by Large Language Models (LLMs) and is based on context information only. The context provided to the LLM includes the error message shown in the console, the URL of the triggering action, and the IAM role of the user signed in the AWS Console. The service always operates within the permissions granted by your role as you operate in the AWS Console, ensuring that privileges are never escalated beyond what are assigned to you.

When you click on Help me resolve button after you have reviewed the analysis, Amazon Q retrieves additional information about the state of the resources in the AWS Account where the error occurred. This is accomplished by interrogating the customer account in various ways. In this phase, the system actively decides which information is still missing and issues interrogation requests against internal services to fulfil the information need. Interrogation is not needed for simple errors, such as Example 1 above, but becomes essential in order to resolve more complex errors, where information from the context proves insufficient.

Given the context, error analysis, user permissions, and results of account interrogation, Amazon Q generates step-by-step Resolution instructions. This step is assisted by LLMs.

After implementing and validating the steps provided by Amazon Q to resolve the error in the console, you have the option to provide feedback of your experience.

A flow diagram illustrating an error resolution process using Amazon Q. The process begins with an error. The user then diagnoses the issue with Amazon Q, which gets context information from the AWS Console and provide an Analysis. The user requests help to resolve the error. The system enriches the prompt interrogation the signed-in user's account. The model generates step-by-step resolution instructions. These instructions go through a validation process before being presented to the user for implementation.

Diagram showing Interactions between User, AWS Console and Amazon Q Developer

Context Information

Contextual information helps the LLMs to generate more relevant and informed outputs. Context is provided to Amazon Q as input from the console automatically. As the basis for all further analysis and decisions, it should be as rich as possible. At a minimum, Amazon Q obtains the error message, the URL for the triggering action, and the IAM role that the signed-in user assumes. The system automatically extracts relevant identifiers from the context. In our running Example 1, the URL may be https://s3.console.aws.amazon.com/s3/bucket/my-bucket-123456/delete?region=us-west-2, from which Amazon Q extracts aws_region = "us-west-2" and s3_bucket_name = "my-bucket-123456".

Beyond this minimum context, Amazon Q can obtain additional information from the console, pertaining to what the user sees on the screen when the error happens, such as content of text fields or widgets in the current UI. Amazon Q can also make use of specific context provided by the underlying service. In the case of Example 2 above, the bucket name is extracted from the URL, the action s3:ListBucket from the error message, and Amazon Q may obtain additional information from IAM about related policies and accept or deny statements.

Interrogating the signed-in user’s Account

Diagnose with Amazon Q functionality is not just a passive receiver of context information, it has built-in capabilities of actively asking for additional information. This includes developing an understanding of resources in the AWS account, and their relationship with the resource experiencing the error. Such interrogation queries are planned by a subsystem based on context information. It provides a low-latency and deterministic approach to find resources and their relationships. This relationship context provided to the LLM, such as EBS volumes attached to an EC2 instance or policies included in the attached IAM role, improves the accuracy of root cause analysis for diagnosing the error.

In the simple running Example 1 where error is due to non-empty S3 bucket, the error message and the console URL contain all the necessary information to proceed, and active interrogation is not required. On the other hand, for the IAM permission error in Example 2, it’s helpful to understand the permissions on the IAM role associated with the resource experiencing the error. Amazon Q can fetch identity-level policies for the role and resource-level policies for the affected resource, based on which it can diagnose the cause of the error, using internal IAM services. To be concrete, the URL for Example 2 may be https://s3.console.aws.amazon.com/s3/buckets/my-bucket-123456?region=us-west-2&bucketType=general&tab=objects, from which Amazon Q extracts region and S3 bucket name. It can also extract the action s3:ListBucket from the error message itself. Based on this information, Amazon Q can fetch bucket policies for my-bucket-123456, identity-level policies for the role, then scan those for presence or absence of the s3:ListBucket action, or call internal IAM services to provide additional information about the cause of access being denied.

This subsystem uses AWS Cloud Control API (CCAPI) which is called on your behalf by Amazon Q with the permissions granted by your IAM Role. As part of onboarding to Amazon Q, the AmazonQFullAccess managed policy is attached to the Role that can access Amazon Q. This managed policy contains the ListResources and GetResource CCAPI IAM permissions. This ensures all Roles given that managed policy will have access to the CCAPI read and list endpoints. If you do not attach the AmazonQFullAccess managed policy to the required roles, you will need to attach the ListResources and GetResource permission directly to the role.

Generating Step-by-step Resolution Instructions

At this point, all acquired information is synthesized by Amazon Q in order to generate useful and actionable resolution instructions. As an illustration, possible sample instructions for the running examples under consideration are listed below. As the models are updated and improved over time, the responses can change.

For Example 1, sample instructions could look like:

  1. Navigate to the S3 console, click “Buckets”, and select the my-bucket-123456 bucket
  2. Click on the “Empty” tab.
  3. If your bucket contains a large number of objects, creating a lifecycle rule to delete all objects in the bucket might be a more efficient way of emptying your bucket
  4. Type “permanently delete” in text input field and confirm that all objects are to be removed.
  5. Retry deleting the my-bucket-123456 S3 bucket.

For Example 2, you may obtain:

  1. Go to the IAM console. Edit the IAM policy attached to the role ReadOnly
  2. Allow for the s3:ListBucket action for resource being the S3 bucket ARN arn:aws:s3:::my-bucket-123456.
  3. Save the updated IAM policy
  4. Refresh the S3 console page to list the objects in the bucket my-bucket-123456

Note that the instructions contain information inferred from the context, such as bucket name my-bucket-123456, instead of placeholders. Instructions returned by Diagnose with Amazon Q are complete and fine-grained enough in order to be followed without any extra effort. In fact, while the service makes use of an LLM to synthesize resolution instructions, Amazon Q uses post-processing to correct frequently occurring mistakes. For example, in Example 2 above, the LLM may have returned the ARN as arn:aws:s3:<region>::<bucket_name>, which would be corrected to what is shown above.

The instructions returned for Example 2 above assume that the reason for the user not being able to list objects is a missing Allow statement in the policies attached to the ReadOnly role. Other root causes could be a Deny statement in a policy attached to the S3 bucket, or to the ReadOnly role. Diagnose with Amazon Q can use account interrogation in order to identify the correct root cause and propose the right resolution. In the example above, it can fetch the policies attached to the ReadOnly role and check whether s3:ListBucket is missing indeed, or fetch policies attached to the bucket bucket-123456.

Validation

One goal for Diagnose with Amazon Q is to attain wide coverage of AWS rapidly, while keeping the quality bar high, so that you obtain useful, actionable advice where ever you obtain an error. An important prerequisite to attain this goal is a robust and flexible evaluation system. Evaluating systems based on Generative AI is challenging due to the large output space (natural language) and non-deterministic behavior.

In a nutshell, our validation system is based on building a large dataset of errors, where each record has a certain number of annotations. Each record contains the context (templatized error message and console URL; meaning that bucket-123456 is replaced by {{s3_bucket_name}}, us-west-2 by {{aws_region}}). Annotations include Infrastructure as Code (CloudFormation) descriptions of the erroneous account state and the triggering action, as well as ground truth responses obtained from expert annotators. These records allow us to simulate the behaviour of variants of our system without human interactions and many times faster than real time (by way of parallelization). We are also developing automated validation metrics for comparing ground truth annotations and system responses, based on which offline evaluations can be run fully automatically.

This validation system allows us to rapidly validate new ideas by comparing them against the current state, while also guarding against regressions. While human experts are still needed to provide annotations of error records, we actively innovate to speed up and simplify these tasks, by building annotation tools which avoid natural language input, have validations built in, and are rather asking to correct system output than providing ground truth annotations from scratch.

Conclusion

The Diagnose with Amazon Q feature of Amazon Q Developer allows you to determine the cause of an error in the AWS Console without needing to navigate to multiple service consoles. By providing tailored, step-by-step instructions specific to your AWS account and error context, Amazon Q Developer empowers you to troubleshoot and resolve issues efficiently. This helps your organization achieve greater operational efficiency, reduce downtime, improve service quality, and free up valuable human resources enabling them to focus on higher-value activities. We also provide you details on how AI and machine learning capabilities work behind the scenes to enable this functionality.

About the authors

Matthias Seeger, Principal Applied Scientist, AWS NGDE Science

Matthias Seeger is a Principal Applied Scientist at AWS.

Marco Frattallone, Sr. TAM, AWS Enterprise Support

Marco Frattallone is a Senior Technical Account Manager at AWS focused on supporting Partners. He works closely with Partners to help them build, deploy, and optimize their solutions on AWS, providing guidance and leveraging best practices. Marco is passionate about technology and helps Partners stay at the forefront of innovation. Outside work, he enjoys outdoor cycling, sailing, and exploring new cultures.

Surabhi Tandon, Sr EAE, AWS Support

Surabhi Tandon is a Senior Technical Account Manager at Amazon Web Services (AWS). She supports enterprise customers achieve operational excellence and help them with their cloud journey on AWS by providing strategic technical guidance. Surabhi is a builder with interest in Generative AI, automation, and DevOps. Outside of work, she enjoys hiking, reading and spending time with family and friends.

Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation

Post Syndicated from Bo Li original https://aws.amazon.com/blogs/big-data/amazon-q-data-integration-adds-dataframe-support-and-in-prompt-context-aware-job-creation/

Amazon Q data integration, introduced in January 2024, allows you to use natural language to author extract, transform, load (ETL) jobs and operations in AWS Glue specific data abstraction DynamicFrame. This post introduces exciting new capabilities for Amazon Q data integration that work together to make ETL development more efficient and intuitive. We’ve added support for DataFrame-based code generation that works across any Spark environment. We’ve also introduced in-prompt context-aware development that applies details from your conversations, working seamlessly with a new iterative development experience. This means you can refine your ETL jobs through natural follow-up questions—starting with a basic data pipeline and progressively adding transformations, filters, and business logic through conversation. These improvements are available through the Amazon Q chat experience on the AWS Management Console, and the Amazon SageMaker Unified Studio (preview) visual ETL and notebook interfaces.

The DataFrame code generation now extends beyond AWS Glue DynamicFrame to support a broader range of data processing scenarios. You can now generate data integration jobs for various data sources and destinations, including Amazon Simple Storage Service (Amazon S3) data lakes with popular file formats like CSV, JSON, and Parquet, as well as modern table formats such as Apache Hudi, Delta, and Apache Iceberg. Amazon Q can generate ETL jobs for connecting to over 20 different data sources, including relational databases like PostgreSQL, MySQL and Oracle; data warehouses like Amazon Redshift, Snowflake, and Google BigQuery; NoSQL databases like Amazon DynamoDB, MongoDB, and OpenSearch; tables defined in the AWS Glue Data Catalog; and custom user-supplied JDBC and Spark connectors. Your generated jobs can use a variety of data transformations, including filters, projections, unions, joins, and aggregations, giving you the flexibility to handle complex data processing requirements.

In this post, we discuss how Amazon Q data integration transforms ETL workflow development.

Improved capabilities of Amazon Q data integration

Previously, Amazon Q data integration only generated code with template values that required you to fill in the configurations such as connection properties for data source and data sink and the configurations for transforms manually. With in-prompt context awareness, you can now include this information in your natural language query, and Amazon Q data integration will automatically extract and incorporate it into the workflow. In addition, generative visual ETL in the SageMaker Unified Studio (preview) visual editor allows you to reiterate and refine your ETL workflow with new requirements, enabling incremental development.

Solution overview

This post describes the end-to-end user experiences to demonstrate how Amazon Q data integration and SageMaker Unified Studio (preview) simplify your data integration and data engineering tasks with the new enhancements, by building a low-code no-code (LCNC) ETL workflow that enables seamless data ingestion and transformation across multiple data sources.

We demonstrate how to do the following:

  • Connect to diverse data sources
  • Perform table joins
  • Apply custom filters
  • Export processed data to Amazon S3

The following diagram illustrates the architecture.

Using Amazon Q data integration with Amazon SageMaker Unified Studio (preview)

In the first example, we use Amazon SageMaker Unified Studio (preview) to develop a visual ETL workflow incrementally. This pipeline reads data from different Amazon S3 based Data Catalog tables, performs transformations on the data, and writes the transformed data back into an Amazon S3. We use the allevents_pipe and venue_pipe files from the TICKIT dataset to demonstrate this capability. The TICKIT dataset records sales activities on the fictional TICKIT website, where users can purchase and sell tickets online for different types of events such as sports games, shows, and concerts.

The process involves merging the allevents_pipe and venue_pipe files from the TICKIT dataset. Next, the merged data is filtered to include only a specific geographic region. Then the transformed output data is saved to Amazon S3 for further processing in future.

Data preparation

The two datasets are hosted as two Data Catalog tables, venue and event, in a project in Amazon SageMaker Unified Studio (preview), as shown in the following screenshots.

Data processing

To process the data, complete the following steps:

  1. On the Amazon SageMaker Unified Studio console, on the Build menu, choose Visual ETL flow.

An Amazon Q chat window will help you provide a description for the ETL flow to be built.

  1. For this post, enter the following text:
    Create a Glue ETL flow connect to 2 Glue catalog tables venue and event in my database glue_db_4fthqih3vvk1if, join the results on the venue’s venueid and event’s e_venueid, and write output to a S3 location.
    (The database name is generated with the project ID suffixed to the given database name automatically).
  2. Choose Submit.

An initial data integration flow will be generated as shown in the following screenshot to read from the two Data Catalog tables, join the results, and write to Amazon S3. We can see the join conditions are correctly inferred from our request from the join node configuration displayed.

Let’s add another filter transform based on the venue state as DC.

  1. Choose the plus sign and choose the Amazon Q icon to ask a follow-up question.
  2. Enter the instructions filter on venue state with condition as venuestate==‘DC’ after joining the results to modify the workflow.

The workflow is updated with a new filter transform.

Upon checking the S3 data target, we can see the S3 path is now a placeholder <s3-path> and the output format is Parquet.

  1. We can ask the following question in Amazon Q:
    update the s3 sink node to write to s3://xxx-testing-in-356769412531/output/ in CSV format
    in the same way to update the Amazon S3 data target.
  2. Choose Show script to see the generated code is DataFrame based, with all context in place from all of our conversation.
  3. Finally, we can preview the data to be written to the target S3 path. Note that the data is a joined result with only the venue state DC included.

With Amazon Q data integration with Amazon SageMaker Unified Studio (preview), an LCNC user can create the visual ETL workflow by providing prompts to Amazon Q and the context for data sources and transformations are preserved. Subsequently, Amazon Q also generated the DataFrame-based code for data engineers or more experienced users to use the automatic ETL generated code for scripting purposes.

Amazon Q data integration with Amazon SageMaker Unified Studio (preview) notebook

Amazon Q data integration is also available in the Amazon SageMaker Unified Studio (preview) notebook experience. You can add a new cell and enter your comment to describe what you want to achieve. After you press Tab and Enter, the recommended code is shown.

For example, we provide the same initial question:

Create a Glue ETL flow to connect to 2 Glue catalog tables venue and event in my database glue_db_4fthqih3vvk1if, join the results on the venue’s venueid and event’s e_venueid, and write output to a S3 location.

Similar to the Amazon Q chat experience, the code is recommended. If you press Tab, then the recommended code is chosen.

The following video provides a full demonstration of these two experiences in Amazon SageMaker Unified Studio (preview).

Using Amazon Q data integration with AWS Glue Studio

In this section, we walk through the steps to use Amazon Q data integration with AWS Glue Studio

Data preparation

The two datasets are hosted in two Amazon S3 based Data Catalog tables, event and venue, in the database glue_db, which we can query from Amazon Athena. The following screenshot shows an example of the venue table.

Data processing

To start using the AWS Glue code generation capability, use the Amazon Q icon on the AWS Glue Studio console. You can start authoring a new job, and ask Amazon Q the question to create the same workflow:

Create a Glue ETL flow connect to 2 Glue catalog tables venue and event in my database glue_db, join the results on the venue’s venueid and event’s e_venueid, and then filter on venue state with condition as venuestate=='DC' and write to s3://<s3-bucket>/<folder>/output/ in CSV format.

You can see the same code is generated with all configurations in place. With this response, you can learn and understand how you can author AWS Glue code for your needs. You can copy and paste the generated code to the script editor. After you configure an AWS Identity and Access Management (IAM) role on the job, save and run the job. When the job is complete, you can begin querying the data exported to Amazon S3.

After the job is complete, you can verify the joined data by checking the specified S3 path. The data is filtered by venue state as DC and is now ready for downstream workloads to process.

The following video provides a full demonstration of the experience with AWS Glue Studio.

Conclusion

In this post, we explored how Amazon Q data integration transforms ETL workflow development, making it more intuitive and time-efficient, with the latest enhancement of in-prompt context awareness to accurately generate a data integration flow with reduced hallucinations, and multi-turn chat capabilities to incrementally update the data integration flow, add new transforms and update DAG nodes. Whether you’re working with the console or other Spark environments in SageMaker Unified Studio (preview), these new capabilities can significantly reduce your development time and complexity.

To learn more, refer to Amazon Q data integration in AWS Glue.

About the Authors

Bo Li is a Senior Software Development Engineer on the AWS Glue team. He is devoted to designing and building end-to-end solutions to address customers’ data analytic and processing needs with cloud-based, data-intensive technologies.

Stuti Deshpande is a Big Data Specialist Solutions Architect at AWS. She works with customers around the globe, providing them strategic and architectural guidance on implementing analytics solutions using AWS. She has extensive experience in big data, ETL, and analytics. In her free time, Stuti likes to travel, learn new dance forms, and enjoy quality time with family and friends.

Kartik Panjabi is a Software Development Manager on the AWS Glue team. His team builds generative AI features for the Data Integration and distributed system for data integration.

Shubham Mehta is a Senior Product Manager at AWS Analytics. He leads generative AI feature development across services such as AWS Glue, Amazon EMR, and Amazon MWAA, using AI/ML to simplify and enhance the experience of data practitioners building data applications on AWS.

Solve complex problems with new scenario analysis capability in Amazon Q in QuickSight

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/solve-complex-problems-with-new-scenario-analysis-capability-in-amazon-q-in-quicksight/

Today, we announced a new capability of Amazon Q in QuickSight that helps users perform scenario analyses to find answers to complex problems quickly. This AI-assisted data analysis experience helps business users find answers to complex problems by guiding them step-by-step through in-depth data analysis—suggesting analytical approaches, automatically analyzing data, and summarizing findings with suggested actions—using natural language prompts. This new capability eliminates hours of tedious and error-prone manual work traditionally required to perform analyses using spreadsheets or other alternatives. In fact, Amazon Q in QuickSight enables business users to perform complex scenario analysis up to 10x faster than spreadsheets. This capability expands upon existing data Q&A capabilities of Amazon QuickSight so business professionals can start their analysis by simply asking a question.

How it works
Business users are often faced with complex questions that have traditionally required specialized training and days or weeks of time analyzing data in spreadsheets or other tools to address. For example, let’s say you’re a franchisee with multiple locations to manage. You might use this new capability in Amazon Q in QuickSight to ask, “How can I help our new Chicago store perform as well as the flagship store in New York?” Using an agentic approach, Amazon Q would then suggest analytical approaches needed to address the underlying business goal, automatically analyze data, and present results complete with visualizations and suggested actions. You can conduct this multistep analysis in an expansive analysis canvas, giving you the flexibility to make changes, explore multiple analysis paths simultaneously, and adapt to situations over time.

This new analysis experience is part of Amazon QuickSight meaning it can read from QuickSight dashboards which connect to sources such as Amazon Athena, Amazon Aurora, Amazon Redshift, Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service. Specifically, this new experience is part of Amazon Q in QuickSight, which allows it to seamlessly integrate with other generative business intelligence (BI) capabilities such as data Q&A. You can also upload either a .csv or a single-table, single-sheet .xlsx file to incorporate into your analysis.

Here’s a visual walkthrough of this new analysis experience in Amazon Q in QuickSight.

I’m planning a customer event, and I’ve received an Excel spreadsheet of all who’ve registered to attend the event. I want to learn more about the attendees, so I analyze the spreadsheet and ask a few questions. I start by describing what I want to explore.

I upload the spreadsheet to start my analysis. Firstly, I want to understand how many people have registered for the event.

To design an agenda that’s suitable for the audience, I want to understand the various roles that will be attending. I select on the + icon to add a new block for asking a question following along the thread from the previous block.

I can continue to ask more questions. However, there are suggested questions for analyzing my data even further, and I now select one of these suggested questions. I want to increase marketing efforts at companies that don’t currently have a lot of attendees in this case, companies with fewer than two attendees.

Amazon Q executes the required analysis and keeps me updated of the progress. Step 1 of the process identifies companies that have fewer than two attendees and lists them.

Step 2 gives an estimate of how many more attendees I might get from each company if marketing efforts are increased.

In Step 3 I can see the potential increase in total attendees (including the percentage increase) in line with the increase in marketing efforts.

Lastly, Step 4 goes even further to highlight companies I should prioritize for these increased marketing efforts.

To increase the potential number of attendees even more, I wanted to change the analysis to identify companies with fewer than three attendees instead of two attendees. I choose the AI sparkle icon in the upper right to launch a modal that I then use to provide more context and make specific changes to the previous result.


This change resulted in new projections, and I can choose to consider them for my marketing efforts or keep to the previous projections.


Now available
Amazon Q in QuickSight Pro users can use this new capability in preview in the following AWS Regions at launch: US East (N. Virginia) and US West (Oregon). Get started with a free 30-day trial of QuickSight today. To learn more, visit the Amazon QuickSight User Guide. You can submit your questions to AWS re:Post for Amazon QuickSight, or through your usual AWS Support contacts.

– Veliswa.

Use Amazon Q Developer to build ML models in Amazon SageMaker Canvas

Post Syndicated from Elizabeth Fuentes original https://aws.amazon.com/blogs/aws/use-amazon-q-developer-to-build-ml-models-in-amazon-sagemaker-canvas/

As a data scientist, I’ve experienced firsthand the challenges of making machine learning (ML) accessible to business analysts, marketing analysts, data analysts, and data engineers who are experts in their domains without ML experience. That’s why I’m particularly excited about today’s Amazon Web Services (AWS) announcement that Amazon Q Developer is now available in Amazon SageMaker Canvas. What catches my attention is how Amazon Q Developer helps connect ML expertise with business needs, making ML more accessible across organizations.

Amazon Q Developer helps domain experts build accurate, production-quality ML models through natural language interactions, even if they don’t have ML expertise. Amazon Q Developer guides these users by breaking down their business problems and analyzing their data to recommend step-by-step guidance for building custom ML models. It transforms users’ data to remove anomalies, and builds and evaluates custom ML models to recommend the best one, while providing users control and visibility into every step of the guided ML workflow. This empowers organizations to innovate faster with reduced time to market. It also reduces their reliance on ML experts so their specialists can focus on more complex technical challenges.

For example, a marketing analyst can state, “I want to predict home sales prices using home characteristics and past sales data”, and Amazon Q Developer will translate this into a set of ML steps, analyzing relevant customer data, building multiple models, and recommending the best approach.

Let’s see it in action
To start using Amazon Q Developer, I follow the Getting started with using Amazon SageMaker Canvas guide to launch the Canvas application. In this demo, I use natural language instructions to create a model to predict house prices for marketing and finance teams. From the SageMaker Canvas page, I select Amazon Q and then choose Start a new conversation.

In the new conversation I write:

I am an analyst and need to predict house prices for my marketing and finance teams.

Next, Amazon Q Developer explains the problem and recommends the appropriate ML model type. It also outlines the solution requirements, including the necessary dataset characteristics. Amazon Q Developer then asks if I want to upload my dataset or I want to choose a target column. I select it to upload my dataset.

In the next step, Amazon Q Developer lists the dataset requirements, which include relevant information about houses, current house prices, and the target variable for the regression model. It then recommended next steps, including: I want to upload my dataset, Select an existing dataset, Create a new dataset or I want to choose a target column. For this demo, I’ll use the canvas-sample-housing.csv sample dataset as my existing dataset.

select_an_existing_dataset

After selecting and loading the dataset, Amazon Q Developer analyzes it and suggests median_house_value as the target column for the regression model. I accept by selecting I would like to predict the “median_house_value” column. Moving on to the next step, Amazon Q Developer details which dataset features (such as “location”, “housing_median_age”, and “total_rooms”) it will use to predict the median_house_value.

Before moving forward with model training, I ask about the data quality, because without good data we can’t build a reliable model. Amazon Q Developer responds with quality insights for my entire dataset.

I can ask specific questions about individual features and their distributions to better understand the data quality.

columns in dataset

To my surprise, through the previous question, I discovered that the “households” column has a wide variation between extreme values, which could affect the model’s prediction accuracy. Therefore, I ask Amazon Q Developer to fix this outlier problem.

After the transformation is done, I can ask what steps Amazon Q Developer followed to make this change. Behind the scenes, Amazon Q Developer applies advanced data preparation steps using SageMaker Canvas data preparation capabilities, which I can review and see the steps so that I can visualize and replicate the process to get the final, prepared dataset for training the model.

After reviewing the data preparation steps, I select Launch my training job.

launch training job

After the training job is launched, I can see its progress in the conversation, and the datasets created.

As a data scientist, I particularly appreciate that, with Amazon Q Developer, Ican see detailed metrics such as the confusion matrix and precision-recall scores for classification models and root mean square error (RMSE) for regression models. These are crucial elements I always look for when evaluating model performance and making data-driven decisions, and it’s refreshing to see them presented in a way that’s accessible to nontechnical users to build trust and enable proper governance while maintaining the depth that technical teams need.

You can access these metrics by selecting the new model from My Models or from the Amazon Q conversation menu:

  • Overview – This tab shows the Column impact analysis. In this case, median_income emerges as the primary factor influencing my model.
  • Scoring – This tab provides model accuracy insights, including RMSE metrics.
  • Advanced metrics – This tab displays the detailed Metrics table, Residuals and Error density for in-depth model evaluation.

Analyze My Model

After reviewing these metrics and validating the model’s performance, I can move to the final stages of the ML workflow:

  • Predictions – I can test my model using the Predictions tab to validate its real-world performance.
  • Deployment – I can create an endpoint deployment to make my model available for production use.

This simplifies the deployment process, a step that traditionally requires significant DevOps knowledge, into a straightforward operation that business analysts can handle confidently.

predictions and deploy

Things to know
Amazon Q Developer democratizes ML across organizations:

Empowering all skill levels with ML – Amazon Q Developer is now available in SageMaker Canvas, helping business analysts, marketing analysts, and data professionals who don’t have ML experience create solutions for business problems through a guided ML workflow. From data analysis and model selection to deployment, users can solve business problems using natural language, reducing dependence on ML experts such as data scientists and enabling organizations to innovate faster with reduced time to market.

Streamlining the ML workflow – With Amazon Q Developer available in SageMaker Canvas, users can prepare data, and build, analyze, and deploy ML models through a guided, transparent workflow. Amazon Q Developer provides advanced data preparation and AutoML capabilities that democratize ML, and allows non-ML experts to produce highly-accurate ML models.

Providing full visibility into the ML workflow – Amazon Q Developer provides full transparency by generating the underlying code and technical artifacts such as data transformation steps, model explainability, and accuracy measures. This allows cross-functional teams, including ML experts, to review, validate, and update the models as needed, facilitating collaboration in a secure environment.

Availability – Amazon Q Developer is now in preview release in Amazon SageMaker Canvas.

Pricing – Amazon Q Developer is now available in SageMaker Canvas at no additional cost to both Amazon Q Developer Pro Tier and Amazon Q Developer Free tier users. However, standard charges apply for resources such as SageMaker Canvas workspace instances and any resources used for building or deploying models. For detailed pricing information, visit the Amazon SageMaker Canvas Pricing.

To learn more about getting started visit the Amazon Q Developer product web page.

— Eli

Introducing the next generation of Amazon SageMaker: The center for all your data, analytics, and AI

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/introducing-the-next-generation-of-amazon-sagemaker-the-center-for-all-your-data-analytics-and-ai/

Today, we’re announcing the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI. The all-new SageMaker includes virtually all of the components you need for data exploration, preparation and integration, big data processing, fast SQL analytics, machine learning (ML) model development and training, and generative AI application development.

The current Amazon SageMaker has been renamed to Amazon SageMaker AI. SageMaker AI is integrated within the next generation of SageMaker while also being available as a standalone service for those who wish to focus specifically on building, training, and deploying AI and ML models at scale.

Highlights of the new Amazon SageMaker
At its core is SageMaker Unified Studio (preview), a single data and AI development environment. It brings together functionality and tools from the range of standalone “studios,” query editors, and visual tools that we have today in Amazon Athena, Amazon EMR, AWS Glue, Amazon Redshift, Amazon Managed Workflows for Apache Airflow (MWAA), and the existing SageMaker Studio. We’ve also integrated Amazon Bedrock IDE (preview), an updated version of Amazon Bedrock Studio, to build and customize generative AI applications. In addition, Amazon Q provides AI assistance throughout your workflows in SageMaker.

Here’s a list of key capabilities:

In this post, I give you a quick tour of the new SageMaker Unified Studio experience and how to get started with data processing, model development, and generative AI app development.

Working with Amazon SageMaker Unified Studio (preview)
With SageMaker Unified Studio, you can discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, and generative AI app building, in a single governed environment.

An integrated SQL editor lets you query data from multiple sources, and a visual extract, transform, and load (ETL) tool simplifies the creation of data integration and transformation workflows. New unified Jupyter notebooks enable seamless work across different compute services and clusters. With the new built-in data catalog functionality, you can find, access, and query data and AI assets across your organization. Amazon Q is integrated to streamline tasks across the development lifecycle.

Amazon SageMaker Unified Studio

Let’s explore the individual capabilities in more detail.

Data processing
SageMaker integrates with SageMaker Lakehouse and lets you analyze, prepare, integrate, and orchestrate your data in a unified experience. You can integrate and process data from various sources using the provided connectivity options.

Start by creating a project in SageMaker Unified Studio, choosing the SQL analytics or data analytics and AI-ML model development project profile. Projects are a place to collaborate with your colleagues, share data, and use tools to work with data in a secure way. Project profiles in SageMaker define the preconfigured set of resources and tools that are provisioned when you create a new project. In your project, choose Data in the left menu and start adding data sources.

Amazon SageMaker Unified Studio

The built-in SQL query editor lets you query your data stored in data lakes, data warehouses, databases, and applications directly within SageMaker Unified Studio. In the top menu of SageMaker Unified Studio, select Build and choose Query Editor to get started. Also, try creating SQL queries using natural language with Amazon Q while you’re at it.

Amazon SageMaker Unified Studio

You should also explore the built-in visual ETL tool to create data integration and transformation workflows using a visual, drag-and-drop interface. In the top menu, select Build and choose Visual ETL flow to get started.

Amazon SageMaker Unified Studio

If Amazon Q is enabled, you can also use generative AI to author flows. Visual ETL comes with a wide range of data connectors, pre-built transformations, and features such as scheduling, monitoring, and data previewing to streamline your data workflows.

Model development
SageMaker Unified Studio includes capabilities from SageMaker AI, which provides infrastructure, tools, and workflows for the entire ML lifecycle. From the top menu, select Build to access tools for data preparation, model training, experiment tracking, pipeline creation, and orchestration. You can also use these tools for model deployment and inference, machine learning operations (MLOps) implementation, model monitoring and evaluation, as well as governance and compliance.

To start your model development, create a project in SageMaker Unified Studio using the data analytics and AI-ML model development project profile and explore the new unified Jupyter notebooks. In the top menu, select Build and choose JupyterLab. You can use the new unified notebooks to seamlessly work across different compute services and clusters. You can use these notebooks to switch between environments without leaving your workspace, streamlining your model development process.

Amazon SageMaker Unified Studio

You can also use Amazon Q Developer to assist with tasks such as code generation, debugging, and optimization throughout your model development process.

Generative AI app development
Use the new Amazon Bedrock IDE to develop generative AI applications within Amazon SageMaker Unified Studio. The Amazon Bedrock IDE includes tools to build and customize generative AI applications using FMs and advanced capabilities such as Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, Amazon Bedrock Agents, and Amazon Bedrock Flows to create tailored solutions aligned with your requirements and responsible AI guidelines.

Choose Discover in the top menu of SageMaker Unified Studio to browse Amazon Bedrock models or experiment with the model playgrounds.

Amazon Bedrock IDE

Create a project using the GenAI Application Development profile to start building generative AI applications. Choose Build in the top menu of SageMaker Unified Studio and select Chat agent.

Amazon Bedrock IDE

With the Amazon Bedrock IDE, you can build chat agents and create knowledge bases from your proprietary data sources with just a few clicks, enabling Retrieval-Augmented Generation (RAG). You can add guardrails to promote safe AI interactions and create functions to integrate with any system. With built-in model evaluation features, you can test and optimize your AI applications’ performance while collaborating with your team. Design flows for deterministic genAI-powered workflows, and when ready, share your applications or prompts within the domain or export them for deployment anywhere—all while maintaining control of your project and domain assets.

For a detailed description of all Amazon SageMaker capabilities, check the SageMaker Unified Studio User Guide.

Getting started
To begin using SageMaker Unified Studio, administrators need to complete several setup steps. This includes setting up AWS IAM Identity Center, configuring the necessary virtual private cloud (VPC) and AWS Identity and Access Management (IAM) roles, creating a SageMaker domain, and enabling Amazon Q Developer Pro. Instead of IAM Identity Center, you can also configure SAML through IAM federation for user management.

After the environment is configured, users sign in through the provided SageMaker Unified Studio domain URL with single sign-on. You can create projects to collaborate with team members, choosing from pre-configured project profiles for different use cases. Each project connects to a Git repository for version control and includes an example unified Jupyter notebook to get you started.

For detailed setup instructions, check the SageMaker Unified Studio Administrator Guide.

Now available
The next generation of Amazon SageMaker is available today in the US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland) AWS Regions. Amazon SageMaker Unified Studio and Amazon Bedrock IDE are available today in preview in these AWS Regions. Check the full Region list for future updates.

For pricing information, visit Amazon SageMaker pricing and Amazon Bedrock pricing. To learn more, visit Amazon SageMaker, SageMaker Unified Studio, and Amazon Bedrock IDE.

Existing Amazon Bedrock Studio preview domains will be available until February 28, 2025, but you may not create new workspaces. To experience the advanced features of Bedrock IDE, create a new SageMaker domain following the instructions in the Administrator Guide.

Give the new Amazon SageMaker a try in the console today and let us know what you think! Send feedback to AWS re:Post for Amazon SageMaker or through your usual AWS Support contacts.

— Antje

Amazon Q Business is adding new workflow automation capability and 50+ action integrations

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/amazon-q-business-is-adding-new-workflow-automation-capability-and-50-action-integrations/

Amazon Q Business, a generative AI–powered assistant designed to enhance productivity across various business applications, became generally available earlier this year. Since its launch, Amazon Q Business has been helping customers tackle the challenges of improving workforce productivity.

In this post, we have two announcements for Amazon Q Business:

  1. AI-powered workflow automation in Amazon Q Business (coming soon)
  2. Supports for more than 50 action integrations (generally available)

Let’s get started with these new announcements from Amazon Q Business:

AI-powered workflow automation in Amazon Q Business (coming soon)
Organizations handle hundreds, if not thousands, of complex workflows that demand precise, repeatable execution. Automating these workflows has been a time-consuming process, often taking months and requiring specialized expertise. As a result, many potentially valuable business processes remain manual, leading to inefficiencies and missed opportunities.

Available soon, Amazon Q Business will have a new capability to simplify the creation and maintenance of complex business workflows.

With this capability, you only need to describe your desired workflow using natural language, upload a standard operating procedure (SOP), or record a video of the process being performed. Amazon Q Business uses generative AI to automatically author a detailed workflow plan from your inputs in minutes. Then, with the recommended workflow, you can review, test, modify, or approve.

Let’s consider an example of automotive claim processing. This process typically involves manually reading claim emails, reviewing attachments, and creating claims in the system. With the new capability in Amazon Q Business, I can create this workflow more efficiently, reducing the time and complexity typically associated with workflow creation.

First, I upload the relevant SOP.

During the workflow creation process, Amazon Q Business may ask questions to clarify and gather any additional information needed to complete the workflow design.

Based on the provided inputs, Amazon Q Business generates an initial workflow template. As an automation author, I can then customize this workflow using a visual drag-and-drop interface and integrate it with supported third-party applications for testing. The workflow can include API calls, automatic UI actions, execution logic, AI agents, and human-in-the-loop steps to cater to the unique needs of every business process across a wide range of industries and business functions.

When it’s finalized, I can publish the workflow and configure it to run either on a schedule or in response to specific triggers. Once published, I can actively track its performance using a feature-rich monitoring dashboard. This dashboard offers built-in analytics, providing detailed insights into the execution and efficiency of all published workflows.

When executing the workflow, Amazon Q Business uses a UI agent trained on thousands of websites and desktop applications to seamlessly navigate changes to page layouts and unexpected pop-up windows in real time. Amazon Q Business includes UI automation, API integrations, and workflow orchestration in a single system, eliminating the need to integrate multiple products and services to create a complete enterprise workflow automation system.

Supports for more than 50 action integrations
With Amazon Q Business plugins, you have the flexibility to connect to third-party apps and perform specific tasks related to supported third-party services directly within your web experience chat. These plugins are accessible through Amazon Q Apps, a feature within Amazon Q Business that helps you create AI-powered apps that streamline tasks and boost productivity. Additionally, when workflow automation capabilities launch, you will be able to integrate these plugins directly into your workflows.

In this announcement, we’re introducing a ready-to-use library of platforms with over 50 action integrations and 11 popular business applications. These business applications include Microsoft Teams, PagerDuty Advance, Salesforce, ServiceNow, and more. 

To get started with the new integrations, access Amazon Q Business through your existing account and explore the new plugins and action integrations.

With these integrations, you can perform various tasks across multiple applications within the Amazon Q Business web application.

Let’s say I need to create a new opportunity with Salesforce. First, I open my Amazon Q Business web application.

Then, I trigger Amazon Q Business plugins and select the Create Opportunity action.

Then, I ask Amazon Q Business to create an opportunity record.

If the action plugin requires more information, it will prompt me to gather more information.

The Amazon Q Business plugin will automatically create the record for me with the Salesforce action plugin.

From here, I can complete additional tasks, such as associating the opportunity record with the account.

Get started with Amazon Q Business today
The new Amazon Q Business plugins are available today in all AWS Regions where Amazon Q Business is available. The new capability to orchestrate workflows in Amazon Q Business will be available in preview soon.

Boost productivity and innovation in your organization with Amazon Q Business. Learn more about how to get started on the Amazon Q Business documentation page.

Happy building,
— Donnie

New capabilities from Amazon Q Business enable ISVs to enhance generative AI experiences

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/new-capabilities-from-amazon-q-business-enable-isvs-to-enhance-generative-ai-experiences/

Since its launch, companies have been using Amazon Q Business to improve their employees’ productivity with a generative AI–powered assistant that helps them make better decisions based on company data and information. Employees also use various software applications provided by independent software vendors (ISVs) to complete their tasks. Many ISVs are creating their own generative AI features intended to make their users more productive, but ISVs are often limited to data within their own application, resulting in end users still shifting between applications to complete tasks.

Today, we’re excited to announce new Amazon Q Business capabilities for ISVs. ISVs can now integrate with the Amazon Q index to retrieve data from multiple sources through a single API and customize the design of their Amazon Q embedded assistant.

These new capabilities enable ISVs and application developers to rapidly deploy personalized, AI-powered experiences within their applications, leveraging both enterprise knowledge and user context across multiple software-as-a-service (SaaS) applications, while accelerating their generative AI roadmap with Amazon Q Business capabilities.

Enhance your generative AI features with additional data using the Amazon Q index
With this new capability, ISVs can access content and context from outside their application, helping them to build richer experiences, improve engagement and retention, while complementing their existing generative AI and Retrieval Augmented Generation (RAG) workflows using their preferred large language models (LLMs). Importantly, customers maintain full ownership of their index and have complete control over which applications can access their data.

Software providers register their applications with Amazon Q Business to allow their customers to grant access to their indexed data. After verification, software providers can use this additional data to enhance their built-in generative AI features, delivering more personalized responses to customers. Visit the Amazon Q index for software providers web page to learn more.

After ISVs complete their integration with the Amazon Q index, they have two paths to onboard their customers to use this new, cross-application experience.

  1. Onboarding through the ISV’s application — Customers initiate the process through the ISV’s platform. The ISV creates an Amazon Q Business application and index on behalf of each customer. Customers then provide the ISV with credentials to connect additional data sources. In this scenario, the ISV maintains complete control over the onboarding experience and user interface.
  2. Onboarding through AWS Management Console – Customers create their Amazon Q Business application directly through the AWS console, where they can connect data sources and grant ISV access to their index. Verified ISVs will be listed as “data accessors” on the Amazon Q Business console. This verification status is granted when the ISV has completed the necessary verification process mentioned above and is ready to launch their customer experience.

Next, we’ll outline the process for a customer to grant a verified ISV access to their existing index.

After customers create their application and add their index, they can grant access to verified ISVs. They can do this by selecting Data accessors in the left navigation panel and then choosing Add data accessor.

On the Add data accessor page, customer will find the list of all verified ISV applications.

After selecting the ISV application, the customer configures what data the ISV can access. The customer also chooses which users will be granted access to the ISV’s updated features.

After granting access, customers must complete the setup by linking their Amazon Q Business application in the ISV’s admin console. Once completed, ISVs can begin retrieving data from the designated index using the SearchRelevantContent API to retrieve data from the index to enrich their generative AI capabilities. Here’s a sample code snippet to use this API:

import boto3
import pprint
qbiz = boto3.client("qbusiness", region_name="us-east-1", **credentials)
 
Q_BIZ_APP_ID = ${Q_BIZ_APP_ID}
 
Q_RETRIEVER_ID = ${Q_RETRIEVER_ID}
 
Q_DATA_SOURCE_ID = ${Q_DATA_SOURCE_ID}
search_params = {
    'applicationId': Q_BIZ_APP_ID,
    'contentSource': {
        'retriever': {
            'retrieverId': Q_RETRIEVER_ID
        }
    },
    'queryText': 'Order coffee API',
    'maxResults': 5,
    'attributeFilter': {
        'documentAttributeFilter': {
            'andAllFilters': [{
                'equalsTo': {
                    'name': '_data_source_id',
                    'value': {
                        'stringValue': DATA_SOURCE_ID
                    }
                }
            }]
        }
    }
}
search_response = qbiz.search_relevant_content(**search_params)

Customize the design of the embedded assistant
Amazon Q embedded is a capability that helps ISVs extend Amazon Q Business to their end users by embedding an AI-powered assistant into their user interface. This capability helps ISV users complete various tasks, such as summarizing documents and answering questions.

Now, software providers have the option to customize the embeddable generative-AI assistant user interface (UI) with Amazon Q embedded to match their corporate branding. To get started, select Amazon Q embedded in the left navigation panel and choose Customize web experience.

On this page, select Theme to start customizing generative AI assistant UI look and feel, such as configuring the assistant name, welcome message, color scheme, and logo.

Available today
The Amazon Q index and Amazon Q embedded with customizable UI are generally available today in the US East (N. Virginia) and US West (Oregon) AWS Regions, with availability in additional AWS Regions coming soon.

ISVs can now use Amazon Q Business features to innovate and enhance their user experiences with powerful AI capabilities. To learn more about how ISVs can enhance their applications, visit Amazon Q Business page for software providers.

Happy coding!

— Donnie

Investigate and remediate operational issues with Amazon Q Developer (in preview)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/investigate-and-remediate-operational-issues-with-amazon-q-developer/

The growing complexity of modern software makes troubleshooting difficult, requiring deep knowledge and manual work across various systems. This results in slower problem-solving and less efficient operations. More and more customers need automated tools to handle routine tasks and simplify complex processes, so they can resolve issues faster and focus on delivering inovations for their customers.

Today, we’re announcing a new capability in Amazon Q Developer to investigate and remediation operational issues, which is now in preview. This generative AI-powered capability guides you through operational diagnostics and automates root cause analysis for problems in your workloads.

Here’s a quick look at how you can now use Amazon Q Developer for operational investigations.

AWS has more operational experience and scale than any other major cloud provider, delivering cloud services to customers around the world for over 17 years. AWS built this experience into Amazon Q Developer operational capabilities to create and present investigation hypotheses, and guide you through troubleshooting and remediation – capabilities that no other major cloud provider offers.

Get started with operational investigation using Amazon Q Developer
This new capability from Amazon Q Developer seamlessly integrates with Amazon CloudWatch and AWS Systems Manager, providing a unified experience while troubleshooting issues. To get started with this capability, you need to complete some prerequisites. You can learn more on the Get Started with Amazon Q Developer Operational Investigations page.

I’ve completed the setup and configured a CloudWatch alarm to monitor the metrics for my application. After receiving a notification email, I navigate to that alarm in Amazon CloudWatch. I observe that the metric has exceeded its threshold over several time periods.

With this finding, I select Investigate. Then, I have two options: Start new investigation or Add to existing investigation. Because I’m just getting started, I select Start a new investigation and provide some details and notes if necessary.

After I’ve created the investigation, I can view the details by choosing View Details on the banner.

The investigation page is divided into two main sections: the left-hand Feed panel, which contains all findings added during the investigation, and the right-hand Suggestions panel, which displays a list of finding suggestions from Amazon Q Developer to assist in the investigation.

Amazon Q Developer uses its knowledge of my AWS resources to automatically discover the relationships between them and create a topology map of the application. This makes it possible for Amazon Q Developer to follow the architecture and quickly find the component that caused an alarm, helping me get back into production faster than ever before.

As I investigate further, Amazon Q Developer proposes hypotheses based on a series of related metrics from various AWS services such as Amazon DynamoDB, AWS Lambda, Amazon Elastic Container Service (Amazon ECS) and others. I can choose Show reasoning to understand why.

One of the hypotheses suggests that the slowness is caused by throttling on a DynamoDB table, with read and write capacity units frequently exceeding the provisioned limits. I find this hypothesis makes sense, and I can Accept it, which will bring it into my Feed.

With all these findings, I can collect all the supporting data to troubleshoot this issue. In one of the hypotheses from Amazon Q Developer, I can also view suggested actions. I select View actions to understand my options for remediation.

In the Suggested actions menu, Amazon Q Developer proposes AWS Systems Manager Automation runbooks related to the hypothesis. Where applicable, it suggests automated runbooks from the AWS Systems Manager library, which includes over 400 AWS-authored and thousands of customer-authored runbooks to help remediate observed issues. Each runbook defines the actions that Systems Manager performs to help resolve the issue. Additionally, Amazon Q Developer provides relevant documentation links from AWS re:Post articles and AWS Documentation pages.

Here’s the list of suggested actions from Amazon Q Developer. I choose View runbook to understand more on how I can solve this issue by modifying DynamoDB provisioned capacity.

Here, I can read more information on this runbook. It will offer a description of the runbook, including execution history telling me if I ran this runbook successfully in this account in the past.

I can enter the required parameters as defined in the configuration. Under Execution preview segment, I can review a summary highlighting the impact on targeted resources. After confirming the details, I select Execute to implement the necessary changes for my workloads.

After running the runbook, I can see the results, which are then added to my feed.

Another feature I appreciate is the multiple ways to access this capability. For example, in my CloudWatch metrics for my AWS Lambda function, I can initiate an investigation and add findings directly. I can also select the Amazon Q Developer operational investigations icon to open the investigation panel.

This new capability from Amazon Q Developer feels like having an AWS expert available 24/7 to assist with operational troubleshooting. It lowers the barrier to operational experience and saves valuable time and effort.

Now in preview
The new capability of Amazon Q Developer to help you investigate and remediate operational issues is now in preview in the US East (N. Virginia) Region. Transform your operational investigation today and accelerate remediation with Amazon Q Developer. Visit Amazon CloudWatch documentation page to get started.

Happy troubleshooting!

— Donnie

Introducing GitLab Duo with Amazon Q

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/introducing-gitlab-duo-with-amazon-q/

Amazon Q Developer has transformed the traditional development workflow by integrating a wide range of generative AI capabilities within the environments where developers work from. This seamless integration helps to maintain focus while accelerating a wide range of development tasks beyond coding for enhanced productivity.

With its vast community of developers, GitLab is a popular DevSecOps platform where many development teams spend their time building and collaborating on projects. That’s why we are so excited to introduce GitLab Duo with Amazon Q. This is a new integration that brings the power of Amazon Q Developer agent capabilities to GitLab using GitLab Duo, transforming it into a unified development experience powering AI-driven DevSecOps. GitLab Duo with Amazon Q leverages AI agents to assist complex, multi-step tasks such as new feature development and codebase upgrades for Java 8 and 11. It also offers enhanced capabilities for code review and unit testing – all within the same familiar GitLab platform.

Interacting with Amazon Q Developer is straightforward through GitLab quick actions— type /q directly inside either the issue description, a general comment, or a merge request comment to start using it to help you accelerate your daily tasks or tackle more complex workflows.

Let’s have a quick tour.

Feature development
Let me show you first how straightforward it is to start using Amazon Q Developer within your GitLab environment when developing new features or improving existing ones.

Imagine that you are working on a web application and you’ve been assigned the task to create a full signup flow. You can ask Amazon Q Developer to generate the whole code for you based on the contents of the issue by adding the /q dev command as a comment.

invoking q dev

Amazon Q Developer analyzes your entire codebase and generates new code, whether in the form of updates to existing files or entirely new ones. After it’s done, it automatically creates a merge request and adds an entry to the Activity history with a link so it can be reviewed.

q generated solution with merge request

On the merge request review page, you’ll notice two interesting things. The first is that Amazon Q Developer has added a comment giving context about the request with instructions for how to request changes if you want to keep iterating. The second one is a follow-up comment where Amazon Q Developer warns that the generated code contains some third-party source material. It provides you with a file that you can download to look up the original code and decide for yourself whether this is something that you’re happy to include in your codebase or not. This makes it effortless to make use of open source responsibly while keeping records for traceability and audit.

the merge request

Before proceeding, you can look through the code and make in-line comments, much like you would with any other merge request. You can then instruct Amazon Q Developer to make changes to the code based on the comments and continue to iterate like that until you’re fully happy with the results . Let’s imagine that your company’s coding standards include a requirement to implement logging for key operations in your code. Unfortunately, this was not included with the initial requirements in the issue’s description before running the /q dev command. However, you can still use Amazon Q Developer to seamlessly add that code during the review process.

To do this, navigate to the Changes tab, find the relevant code lines, and add in-line comments as you would when reviewing a developer’s merge request. For instance, below line 39, a comment is added stating “add logging” to highlight a part of the code that handles errors when calling the signup API. Below it, another comment is added with only /q dev as the text. This standalone comment triggers a quick action to invoke Amazon Q Developer, so it’s essential to keep it separate. Amazon Q Developer will then generate a new revision based on all the comments provided.

It’s worth noting that the /q dev command can be issued from anywhere in GitLab where comments are supported. Although it was convenient to add it here on line 39, the outcome would be the same if the command was issue as a comment on the Overview page, or against any other line of code in the Merge requests page.

After it’s done, Amazon Q Developer notifies you by adding another comment to the merge request history. Again, it also notifies that the generated content contains open source code providing more information about it so you can review it prior to accepting the merge. Upon closer inspection, it’s clear that it has used the logger library, which makes perfect sense considering the request.

Reviewing the code, it’s impressive to see that Amazon Q Developer didn’t only add the calls to log operations where they happen, but also used context to add the relevant log levels, such as info, and warning. Moreover, it also modified the code in other places to make sure the build doesn’t break. For example, it added the import statement at the top of the file and initiated the logger variable.

By using this new development flow, you can move much faster from requirements to code by relying on Amazon Q Developer to help get the tasks done from the convenience of your GitLab environment. After submitting a merge request though, it’s time to perform a code review. Again, you can also use GitLab Duo with Amazon Q Developer to help you accelerate and improve the quality of that process.

Performing code reviews
Let’s work with a different code base, in this case, a Java application. To initiate the assisted code review process, in the merge request overview page, you can submit a comment with the text of /q review. Amazon Q Developer will add an automatic comment to the history informing that it has initiated a review of the merge. It scans all changes looking for security vulnerabilities, quality issues such as code that doesn’t follow best practices, and any other potential problems with the code.

After it’s finished, it will add each finding as a comment that includes a snippet of the problematic code found, a description of the issue, and a severity rating.

You can then take it one step further and ask Amazon Q Developer for a fix! Reply to the findings comment by entering the /q fix command and it will inform you that it is generating a fix for the issue before following it up on the same thread with a solution that you can review. It provides you with a diff view of the changes and an opportunity to accept and commit them.

Upgrading legacy code
In addition to helping you with new code and features, GitLab Duo with Amazon Q Developer can also help automate and accelerate code base migration from Java 8 or 11 to Java 17. Start by creating a new issue and give it a descriptive title such as “Upgrade project to Java 17”. Then, in the Description field, add the command /q transform.

After you create the issue, Amazon Q Developer will follow the same pattern as before and add a comment to the issue’s history to inform you that it’s working on migrating the code base. This comment will be updated after Amazon Q Developer is finished and contain a link to the merge request much like we encountered earlier. It’ll also generate a migration plan that you can review while you wait. The plan contains a collapsible step-by-step list of actions to be taken with detailed information plus links that you can use for further reading.

The merge request produced is rich in details too. It contains general stats such as the number of lines of code that were migrated and the total time taken, among others. It also has a full report with links that you can you use to navigate to different sections so you can read the build log summary, review changes in dependencies, inspect all files changes, and more.

When yo’re ready to review the code, you can accept changes partially or fully, much like with any other merge request.

Conclusion
GitLab Duo with Amazon Q bring together the most comprehensive DevSecOps platform with the most capable generative AI powered assistant for software development. Together, GitLab Duo and Amazon Q offer a seamless developer experience with new capabilities to accelerate feature development and transform workloads, all within the same familiar GitLab environment that developers are used to.

Things to know

  • Getting started – GitLab Duo with Amazon Q is available in preview for GitLab self-managed customers with an Ultimate subscription. Read more on how to get started to learn more about it.
  • Availability – GitLab Duo with Amazon Q is available now under preview for all GitLab self-managed customers with an ultimate tier subscription.

Matheus Guimaraes | @codingmatheus

Enhance your productivity with new extensions and integrations in Amazon Q Business

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/enhance-your-productivity-with-new-extensions-and-integrations-in-amazon-q-business/

Today, we’re announcing a new capability from Amazon Q Business to seamlessly access your assistant within popular web browsers and productivity tools. This helps you save time and complete your work and tasks more efficiently without having to leave your preferred applications.

Now, you can use Amazon Q Business directly from your web browser and other supported messaging and collaboration applications. You can quickly gather insights, review information, and ask questions. For example, you can effortlessly analyze and summarize content, get explanations on complex topics, or create meeting summaries without switching between applications.

Let’s get started
Let me walk you through how to get started with the new browser extensions and integrations. First, let’s look at the browser extensions. The following screenshot shows how it looks.

As an administrator, I need to enable the browser extensions for users of my Amazon Q Business application. To do that, I navigate to my Amazon Q Business application dashboard and select Integrations under the Enhancements section in the left navigation pane.

Then, on the Integrations page, select Edit in the Browser extensions section.

I select the available options in the Browsers section and choose Save. After I’ve enabled these options, my users will receive notification emails prompting them to install the extension.

Now, I’m switching to a user perspective of the Amazon Q Business application. I’ve received an email with a link to the Amazon Q Business web application. I visit the link and sign in to the Amazon Q Business web application. Here, I see a banner with information and a link to install the extension for my browser. I select the Install extension button.

Then, I navigate to the Chrome Web Store and install the browser extension.

After I have installed the browser extension, I sign in to my Amazon Q Business application using the same URL and credentials I use to access the web application.

Now, I can chat with Amazon Q Business apps whenever I visit any webpage. For example, I can ask it to summarize the current website for me.

The following image shows the result.

Application integration with Amazon Q Business
With Amazon Q Business, you can get AI-powered assistance and information not only when browsing, but also when collaborating with your teams. Now, you can integrate Amazon Q Business with supported third-party applications, making it an always-ready productivity and creativity teammate in your conversations.

To add third-party applications to Amazon Q Business, I need to navigate to the Integrations page and choose Add integration.

Here, I find all available integrations that I can use. For this demo, I select Slack.

I fill in all the required details, including the Slack workspace team ID, which you can obtain by following the steps outlined on the Slack documentation page.

After the integration is successfully created, I need to deploy this integration as a Slack bot. From the Integrations page, I select the integration and complete the integration process in the Slack platform. With all the required steps completed, now I can now add the app into my Slack workspace.

Here’s a quick video showing how I use this integration to interact with Amazon Q Business on Slack.

As someone who juggles multiple tools and platforms daily, this new capability unlocks various possibilities for me to improve my productivity. The ability to access AI assistance and perform cross-application tasks without leaving my current workspace helps me save time and maintain focus.

Additional things to know

  • Supported browser extensions – At launch, the Amazon Q Business browser extension supports Chromium-based web browsers such as Google Chrome and Microsoft Edge. It also supports the Mozilla Firefox web browser.
  • Application integration support – For third-party applications, at launch, Amazon Q Business integrations support Slack and Microsoft Teams.
  • Availability – This new capability is available in AWS Regions where Amazon Q Business is available.

Get started today and experience an exciting opportunity to enhance your productivity and streamline cross-application workflows. Learn more on the Amazon Q Business page.

Happy building,
— Donnie

Newly enhanced Amazon Connect adds generative AI, WhatsApp Business, and secure data collection

Post Syndicated from Elizabeth Fuentes original https://aws.amazon.com/blogs/aws/newly-enhanced-amazon-connect-adds-generative-ai-whatsapp-business-and-secure-data-collection/

Today, Amazon Connect introduces a set of new features that help businesses enhance their contact center operations through generative AI, advanced security features, and streamlined bot management. These innovations help businesses deliver better customer experiences by creating more time and space for meaningful human interactions, while maintaining security and compliance.

Contact center managers continually face challenges in optimizing self-service resolution rates, evaluating agent performance efficiently, and maintaining data privacy compliance. Additionally, creating and managing conversational AI experiences often requires specialized expertise and complex integrations across multiple services.

To address these challenges, Amazon Connect introduced key features such as generative AI–powered customer segmentation for targeted campaigns, native WhatsApp Business messaging for omnichannel support, secure collection of sensitive customer data in chat interactions, simplified conversational AI bot management in the Amazon Connect interface, and new enhancements to Amazon Q in Connect. Amazon Connect also added new analytics capabilities through Amazon Connect Contact Lens to help optimize bot performance and contact center operations.

Here are the new capabilities that will help you create more personalized and efficient customer experiences while maintaining the highest standards of data security and operational excellence.

Generative AI powered features
Amazon Connect integrates new generative AI capabilities to automate and enhance customer interactions, enabling smarter targeting and more efficient contact center management.

Generative AI segmentation and trigger-based campaigns – Uses generative AI–powered assistance to create customer segments using conversational prompts. This allows businesses to create precise customer segments using natural language descriptions, making it easier to identify and reach specific customer groups. Trigger campaigns enable organizations to communicate with their customers based on specific customer events, such as cart abandonment.

You can also start with ready-to-use suggestions.

Simplify conversational AI bot creation and enhance them with Amazon Q in Connect – Create, edit, and manage conversational AI bots powered by Amazon Lex directly within the Amazon Connect web interface. You can now enhance these bots with Amazon Q in Connect, a generative AI–powered assistant for customer service. Amazon Q in Connect now supports end-customer self-service interactions across interactive voice response (IVR) and digital channels, in addition to assisting contact center agents with recommended responses and actions.

This integration extends beyond traditional voice and chatbot Amazon Lex capabilities by providing advanced conversational abilities via large language models (LLMs). The system intelligently searches configured knowledge bases, customer information, web content, and third-party application data to respond to customer questions when they don’t match predefined intents. Administrators can set custom guardrails for their instance, defining restrictions on response generation and monitoring Amazon Q in Connect performance.

Generative AI–powered automated evaluations: Supervisors can automatically evaluate up to 100 percent of contacts using generative AI.

Generative AI–powered contact categorization: Improves existing semantic match functionality using natural language intents.

Improved interfaces and tools
Enhanced capabilities for bot management and monitoring, simplifying the creation and optimization of automated experiences.

Amazon Connect for WhatsApp Business messaging – Natively integrate with WhatsApp Business messaging so customers can receive support over WhatsApp in addition to existing Amazon Connect channels such as voice, SMS, chat, and Apple Messages for Business. This addition to Amazon Connect omnichannel capabilities helps businesses meet customers on their preferred communication channel while maintaining consistent service delivery and management within the Amazon Connect application.

Contact Lens conversational AI bot dashboards – Offers analytics to monitor the performance of your conversational AI bots built in Amazon Connect.

Self-service voice (IVR) recording and interaction logs on contact details – Provides comprehensive records of self-service interactions, including audio recordings.

Improved intraday forecasts – Allows comparison of intraday forecasts against previously published forecasts.

Salesforce Contact Center with Amazon Connect (Preview) – Natively integrates the digital channels and unified routing of Amazon Connect into Salesforce customer relationship management (CRM) system. This new offering allows companies to use a single routing and workflow system for both Amazon Connect and Salesforce channels, intelligently directing calls, chats, and cases to the appropriate self-service or agent interaction. If you’re interested, sign up to join the preview.

Enhanced security for chat
New features that enhance security and compliance in chat interactions, enabling secure handling of sensitive information.

Collection of sensitive customer data within chats – Amazon Connect chat and messaging now includes a data privacy option that enables secure handling of sensitive customer information during chat interactions. This feature protects personally identifiable information (PII) and payment card industry (PCI) data, promoting compliance with data protection regulations.

Key benefits
The latest features of Amazon Connect combine generative AI, enhanced security, and streamlined bot management to help businesses:

Transform customer experience – Amazon Connect elevates customer interactions through AI–powered segmentation, enabling personalized engagement strategies. The new WhatsApp Business messaging expands omnichannel support capabilities, meeting customers on their preferred channel. Additionally, advanced bot capabilities, including Amazon Q in Connect, enhance self-service resolution rates, delivering more efficient customer experiences.

Enhance security and operations – Contact centers can now strengthen their security posture with PCI-compliant chat interactions while maintaining operational efficiency. Custom AI guardrails promote appropriate response generation, while the simplified bot management interface eliminates the need for specialized expertise. Analytics and forecasting capabilities provide comprehensive performance monitoring, enabling data-driven decision-making for optimal contact center operations.

Pricing and availability – These features are available today in all AWS Regions where Amazon Connect is supported. For pricing, visit the Amazon Connect Pricing. For implementation guidance, visit the Amazon Connect documentation.

— Eli

How SmugMug Increased Data Modeling Productivity with Amazon Q Developer

Post Syndicated from Will Matos original https://aws.amazon.com/blogs/devops/how-smugmug-increased-data-modeling-productivity-with-amazon-q-developer/

This post is co-written with Dr. Geoff Ryder, Manager, at SmugMug.

Introduction

SmugMug operates two very large online photo platforms: SmugMug and Flickr. These platforms enable more than 100 million customers to safely store, search, share, and sell tens of billions of photos every day. However, the data science and engineering team at SmugMug and Flickr often faces complex data modeling challenges that require significant time to resolve.

These challenges arise due to several factors. First, the team has to contend with diverse datasets from different sources. Additionally, the database schema and tables are highly complex, and the team needs to quickly understand application (PHP) code and database table structures in order to generate the necessary complex database queries. Specifically, SmugMug uses Amazon Redshift as its cloud data warehouse to analyze patterns in petabyte-scale data stored in Amazon S3, as well as transactional data in Amazon Aurora and Amazon DynamoDB. This allows them to generate dozens of business reports daily.

However, the complexity increases further as many database tables also need to be imported from third-party organizations into Amazon Redshift, where they are joined with SmugMug and Flickr’s internal tables. In extreme cases, properly modeling all these database tables and handling issues like granularity, cardinality, timestamps and missing data could take years – an impractical timeline for the business. We are excited to walk through SmugMug’s data modeling use cases and how SmugMug uses Amazon Q Developer to improve the data science and engineering team’s productivity.

Discovering Amazon Q Developer

SmugMug was one of the first customers to pilot Amazon Q Developer (previously Amazon CodeWhisperer), the most capable AI-powered assistant for software development that re-imagines the experience across the entire software development lifecycle, making it easier and faster to build, secure, manage, optimize, operate, and transform applications on AWS. There are multiple Amazon Q Developer use cases at SmugMug and Flickr, such as using Amazon Q Developer agent (/dev) for software development (i.e. generating implementation plans and the accompanying code), generating inline code suggestions, asking Amazon Q Developer in chat about AWS services and best practices, and analyzing AWS usage and costs for Cloud Financial Management (CFM) needs. For the data science and engineering team specifically, the key feature is chatting with Amazon Q Developer in integrated development environments (IDEs) like Intellij DataGrip. The data analysts and data scientists at SmugMug and Flickr ask questions in Amazon Q Developer chat to analyze database schemas, generate data model diagrams from DDL (Data Definition Language) statements, convert queries between languages, automatically generate complex database queries for data analysis, generate code to validate table contents, and predict trends using ML (Machine Learning).

Implementing Amazon Q Developer

To solve the data modeling challenges SmugMug faced, the team collaborated closely with their AWS Account Team, AWS Professional Services, and the Amazon Q Developer service team to create and test a data modeling assistant solution using Amazon Q Developer.

As a first step, the data modeler needs to bring the right metadata to bear. For simpler cases, the commands “show view myschema.v” or “show table myschema.t“ retrieve DDL schema information about the specified view or table from Amazon Redshift into the IDE console.

Here’s an example using simulated data for a hypothetical company. For this typical company that handles orders for products, the result of typing “show table sample.orderinfo” and “show table sample.skuinfo”might be:

Image of SQL statement generated by the show table statement. "CREATE TABLE sample.skuinfo ( sku_id bigint ENCODE raw, sku_vendor bigint ENCODE az64, sku_category character varying(18) ENCODE lzo, sku_description character varying(255) ENCODE lzo, date_sku_created timestamp without time zone ENCODE az64, date_sku_updated timestamp without time zone ENCODE az64, pipeline_inserted_at timestamp without time zone ENCODE az64 ) DISTSTYLE KEY SORTKEY ( sku_id );"

Image of SQL statement generated by the show table statement. "CREATE TABLE sample.orderinfo ( order_id bigint ENCODE raw, shipper_id bigint ENCODE az64 distkey, product_id bigint ENCODE az64, quantity_ordered integer ENCODE az64, date_order_placed timestamp without time zone ENCODE az64 ) DISTSTYLE KEY SORTKEY ( order_id );"

This DDL text is now in the open tab. By selecting the text to highlight it, that DDL text becomes part of the context that Amazon Q Developer sees. The modeler can start asking questions about them in the Amazon Q Developer chat window in the IDE.

Diagram showing what is considered part of the context included in a request including the RAG query result, related documents when using the at-workspace key word, the highlighted text in the IDE open tab,the chat history, and the prompt.

In complex scenarios, establishing the correct modeling context requires a combination of schema information, legacy SQL, application source code in various programming languages, sample values, and natural language documentation. Amazon Q Developer addresses this by creating a local index of relevant files and content. When a question is asked using @workspace, this index is consulted to identify and include pertinent sections of code and information in the request. (See this article for additional details on workspace). The prompt plays a crucial role in measuring similarity, so providing comprehensive context within it is essential. To optimize this process, the IDE settings feature a tunable workspace index function, allowing for enhanced performance in identifying and incorporating relevant context.

Image showing the Amazon Q Settings window where you enable the Workspace feature by checking the "Workspace index" box. You can also change the number of worker threads used, and the maximum workspace index size in MB.

Workspace Index Settings

By adopting Amazon Q Developer as a team, we are able to jointly develop and share proprietary prompt text to address the four steps in our modeling process, as follows.

Step 1. Define the goal for the data modeling project

From prior knowledge, sketch a high-level goal for a data model. Gather the data for it manually, or by e.g. querying a vector database and adding its documents to the project.

For this example, we choose as the goal to compute aggregated metrics from a new table or view composed of two existing tables, sample.orderinfo and sample.skuinfo. These contain simulated data about product sales that are common to many companies. The order table is in the style of a fact table that logs customer orders, and the stock keeping unit (SKU) table is a dimension table that provides additional data points of interest about each order. The order and SKU information need to be combined by a join operation before we can compute the metrics. We would like Amazon Q Developer to tell us how to write that SQL join statement.

Step 2. Conduct an exploratory analysis and generate candidates

Next, prompt Amazon Q Developer for candidate foreign keys to join the tables, and for SQL code to execute those joins. Generate an entity-relationship diagram (ERD) as a visual aid. Prompts do not have to be complicated. For example:

@workspace What columns of database tables sample.orderinfo and sample.skuinfo 
would be best to join the two tables? Provide SQL code for the join. Draw an 
entity relationship diagram that shows the joins between the two tables, and 
includes only the fields involved in the join. Add a crow's foot cardinality 
marker to indicate a 1:many relationship, and add it next to the high 
cardinality table.

Image with the first part of the response to the prompt with the following text: "Based on the table schemas, sku_id is the appropriate column to join these tables. The relationship is likely one-to-many (1:M) where one SKU can appear in multiple orders. Here's the SQL join: SELECT o.order_id, o.sku_id, s.sku_description FROM sample.orderinfo o JOIN sample.skuinfo s ON o.sku_id = s.sku_id;

Image with the second part of the response to the prompt with the ASCII relationship diagram showing the join relationship.

Each time tables are joined together, new aggregated metrics become available to drive business insights. Now, for instance, we can find the top selling SKUs in October thanks to our results:

Image shows the top 5 results from the prior query showing the top skus in October.

Sometimes we need to look at code written in languages other than SQL to complete the data model. For example, the names of some vendors this company works with happen to appear in application PHP code as human readable strings, but are saved in the application database as numbers. The analytics data staged in Redshift only contain the numbers. So, we pull a copy of the PHP text file into @workspace, and ask Amazon Q Developer to translate the relevant string-integer mappings into a SQL case statement.

Image shows the selected PHP code with a switch statement mapping Vendor Ids to Vendor Names.

PHP Switch statement showing the mapping of Vendor Ids to String Names.

I am a Redshift database administrator and I am working on a data modeling 
problem. I would like to write SQL statements to join tables sample.orderinfo 
and sample.skuinfo. Please write that SQL to join the two tables. Also, I 
would like to write a SQL case statement to recover all string values defined 
in PHP that are represented as integer values in the database table.

The output of that prompt is shown below.

Image showing the updated SQL query that maps the Vendor Id to the Vendor Name.

Amazon Q Developer automatically detected the PHP switch case statement, converted to SQL, and added it to the final query. Many other programming languages are supported, and modelers should try this technique with other kinds of source code. Note that data scientists and analysts may not know where to look in complex application code for these details, so this discovery-plus-code translation step is a net new benefit to our company that is only possible thanks to Amazon Q Developer.

Step 3. Create code to test the analysis

Now we request SQL source code for a battery of small test queries. These can return cardinality, grain, arithmetic, and null count results.

Please write a short SQL test to compute counts of the key fields that are used 
in the joins, which will verify the cardinality assignments indicated in the 
entity relationship diagram above. The SQL test should compare distinct counts 
to total counts and null counts when it verifies the cardinality.

Image of resulting SQL queries to check cardinality.

Step 4. Validate the results of the analysis

Run the test queries to see if the candidate solution from step 2 meets our goals. The “Insert at cursor” button at the bottom of the response is handy for this. The data modeler can easily spot an error in the join logic and ERD from inspecting the output of the test query. (Or, if it’s hard to interpret the results, keep making the test queries simpler.) If errors arise from the AI misinterpreting or miscalculating a result, or from a vaguely worded prompt, simply adjust the prompt in step 2 to fix the known errors, and repeat steps 2 – 4.

Image showing the query results from the cardinality query.

After a few iterations, taking from seconds to at most tens of minutes each, the modeling errors have been worked out and we arrive at a valid production query.

Key Benefits and Results

With this Amazon Q Developer powered solution and iterative approach, SmugMug has achieved highly accurate data modeling results across numerous database tables. Once the correct modeling configuration is established, various useful outputs may become available.

We already described production SQL, unit tests, and ERDs for documentation. By the end of the process, because Amazon Q Developer has a good understanding of the data it just modeled in its chat history, it will also generate useful Python machine learning programs to predict business trends. Here is a prompt for that, and a partial screenshot of the Python output:

Please write Python code to implement a linear regression that predicts the 
quantity_ordered value based on other fields in the data set. Choose predictor 
variables that are less likely to cause multi-collinearity problems.

Image showing the python code generated to predict quantity_ordered value.

This only shows the model training step, but the full response included all library imports, a Redshift query, feature engineering steps, ML performance metrics, and code for plotting the metrics. And the AI can produce other types of predictive models. For example, you can try:

Please write Python code to implement an XGBoost model that predicts the 
quantity_ordered value based on other fields in the data set.

Ultimately, the solution has improved team productivity for both existing and new team members, while maintaining legacy knowledge needed to onboard new team members more efficiently. Key benefits include:

  1. Reducing SmugMug data analyst and scientist’s time spent on data modeling tasks from days to hours, allowing them to reallocate this time to other high-priority projects.
  2. Automating the generation of BI documentation and predictive ML, also saving crucial time.
  3. Providing net new value by translating application code constant definitions into SQL. Due to organizational boundaries, we would not have achieved this without an assist from the AI.

Future Plans and Expansion

SmugMug conducted the initial data modeling use case testing with over a dozen data science team members and analysts. We are moving on to analyze more complex tables and data schemas, and generating Python code in Amazon SageMaker for ML tasks like data preparation, training, inference, and MLOps. From our experience, Amazon Q Developer has become a preferred internal tool for development that has a data modeling component, and its use continues to expand to different groups around the company.

For SmugMug’s data modeling projects, we continue to enhance the four-step process described above. In order to gather the most relevant context to solve a problem, we build vector database collections to pull from schemas, older SQL code, application source code, BI tool content, and curated documentation. The vector search operation surfaces the right content, and spares data modelers from manually searching in different code archives. We use ChromaDB to do the searches, and bring the results from ChromaDB into the workspace as additional files.

Conclusion

Using Amazon Q Developer for data modeling use cases, SmugMug has managed to increase data science and engineering team productivity by up to 100% when compared to prior workflows. To explore how Amazon Q Developer can benefit your organization, get started here. If you have questions or suggestions, please leave a comment below.

About the Authors

Image of Dr. Geoffrey Ryder

Dr. Geoffrey Ryder

Dr. Geoff Ryder serves as the Manager of Data Science and Engineering at SmugMug, where he leads Team Prophecy in managing the company’s cloud-based data warehouse and analytics platforms. With a focus on leveraging the best AI tools, his team empowers photography clients to enhance their sales of both physical and digital photographic products. Geoff brings over two decades of experience in technical and business roles across Silicon Valley companies, and holds a PhD in Computer Engineering from UC-Santa Cruz.

Will Matos

Will Matos is a Principal Specialist Solutions Architect at AWS, revolutionizing developer productivity through Generative AI, AI-powered chat interfaces, and code generation. With 25 years of tech experience, and over 9 years with AWS, he collaborates with product teams to create intelligent solutions that streamline workflows and accelerate software development cycles. A thought leader engaging early adopters, Will bridges innovation and real-world needs.

Sreenivas Adiki

Sreenivas Adiki is a Sr. Customer Delivery Architect in ProServe, with a focus on data and analytics. He ensures success in designing, building, optimizing, and transforming in the area of Big Data/Analytics. Ensuring solutions are well-designed for successful deployment, Sreenivas participates in deep architectural discussions and design exercises. He has also published several AWS assets, such as whitepapers and proof-of-concept papers.

Kevin Bell

Kevin Bell is a Sr. Solutions Architect at AWS based in Seattle. He has been building things in the cloud for about 10 years. You can find him online as @bellkev on GitHub.

Corey Keane

Corey Keane is a Media and Entertainment (M&E) Sr. Account Manager at AWS. Corey has held a number of positions at Amazon and AWS throughout his 8 years with the company across M&E—including technical business development for strategic partnerships with international game developers, in addition to his current role managing AWS customers in the Media vertical. He leans on his pan-Amazon experience from working on other teams to identify new partnerships between our customers and other Amazon businesses to bring disruptive products to market.

Dissecting the Performance Gains in Amazon Q Developer agent for code transformation

Post Syndicated from Jonathan Vogel original https://aws.amazon.com/blogs/devops/dissecting-the-performance-gains-in-amazon-q-developer-agent-for-code-transformation/

Amazon Q Developer Agent for code transformation is an AI-powered tool which modernizes code bases from Java 8 and Java 11 to Java 17. Integrated into VS Code and IntelliJ, Amazon Q simplifies the migration process and reduce the time and effort compared to manual process. It proposes and verifies code changes, using AI to debug compilation errors. In this blog post, we’ll explore recent improvements to our code transformation agent, particularly its enhanced debugging capabilities. The enhanced debugger agent significantly improves transformation efficiency and quality compared to the existing debugger.

How Amazon Q transforms Java applications

To upgrade Java codebases, the code transformation agent takes the source code input and verify the build and test in source Java version. It then uses deterministic tools to apply code changes, followed by building and testing the changed code in the target Java version. If errors occur in this stage, a generative AI-based system debugs and resolves the compilation errors. Until today, the debugger resolves each error one by one, locating the code file with the error in the codebase, and fixing it. This debug step iterates until all compilation errors are solved or the maximum number of iterations is reached.

A flowchart diagram illustrating Amazon Q's code transformation process for accelerating Java upgrades to version 17. The workflow begins with source code input, flowing through a transformation engine that applies deterministic tools and generative AI, followed by build/test verification cycles and AI-powered debugging to resolve any compilation errors.

As an example, if, as the result of a library upgrade, an import statement is missing or wrong, the AI debugger will re-build, iterate to find all the references in multiple files one by one, and update each reference to resolve the error. Refer to this blog “Three ways Amazon Q Developer agent for code transformation accelerates Java upgrades” for detailed explanation of each transformation step. This approach has helped Q Developer customers achieve accelerations of migration effort by over 40%.

Improving the debugging capabilities of code transformations

To further improve the ability of Q Developer to generate error-free code, we’ve just released multiple foundational improvements to the AI debugger.

  • Multi-error context: the debug AI can now take multiple build errors into consideration, which provides more context, leading to better solution discovery.
  • More tools available for the AI: compared to simply localizing error to a single file and fixing the error previously, the agent can now execute multi-file solutions by exploring the codebase and operating on multiple files.
  • Inter-iteration memory: the debugger AI now remembers previous errors, which contributes to debugging new errors.
  • Intelligent backtracking: the debugger AI can now recognize if the current solution path leads to a dead end, in which case the agent can roll back to the previous state.

To implement these capabilities, the debugger AI is re-architected as a multi-agent system. A memory management agent is responsible to analyze last iteration results and append the relevant portions to the inter-iteration memory. A critic agent is responsible to analyze progress and provide additional information to the debugger agent and, if a dead end is detected, rollback the progress to a previous state. A debugger agent, analyzes the memory and the critique from the previous agents and modifies or updates the plan to fix the remaining errors in the codebase. The debugger agent has its disposal a set of generic and specialized tools to browse and explore the codebase, edit source files, trigger builds, add dependencies, and so on. It is important to note that the agent only has access to the files and tools related to the transformation task, which limits hallucinations and drive towards progress.

Let’s examine how the agent handles recurring issues across multiple files with these improvements. Consider a scenario where several Java files are missing the same import statement after upgrading from Java 8 to Java 17. This happens when you upgrade from older Java collections (like Vector and Enumeration) to modern streaming operations. The system is capable of helping you update these patterns automatically. The agent is now able to intelligently detect this pattern and implement a comprehensive solution across all affected files. Suppose we have three Java files that use the java.util.stream.Collectors class, but the import is missing in each:

File1.java:

public class File1 {
    public List<String> process(List<String> input) {
        return input.stream()
            .filter(s → s.length() > 5)
            .collect(Collectors.toList()); // Error: Cannot resolve symbol 'Collectors'
    }
}

File2.java:

public class File2 {
    public Map<String, Long> countWords(List<String> words) {
        return words.stream()
            .collect(Collectors.groupingBy(
                word -> word.toLowerCase(),
                Collectors.counting()
            )); // Error: Cannot resolve symbol 'Collectors'
    }
}

File3.java:

public class File3 {
    public String concatenate(List<String> strings) {
        return strings.stream()
            .collect(Collectors.joining(", "));
            // Error: Cannot resolve symbol 'Collectors'
    }
}

After the agent detects the common issue and applies the fix, all three files would be updated as follows:

File1.java (after fix):

import java.util.stream.Collectors;

public class File1 {
    public List<String> process(List<String> input) {
        return input.stream()
            .filter(s -> s.length() > 5)
            .collect(Collectors.toList());
    }
}

File2.java (after fix):

import java.util.stream.Collectors;

public class File2 {
    public Map<String, Long> countWords(List<String> words) {
        return words.stream()
            .collect(Collectors.groupingBy(
                word -> word.toLowerCase(),
                Collectors.counting()));
    }
}

File3.java (after fix):

import java.util.stream.Collectors;

public class File3 {
    public String concatenate(List<String> strings) {
        return strings.stream()
            .collect(Collectors.joining(", "));
    }
}

In this example, the agent has identified that the same import statement (import java.util.stream.Collectors;) was missing in all three files. It then applied the fix consistently across all affected files, demonstrating its ability to recognize patterns and implement solutions efficiently across the entire codebase, avoiding different solutions attempts for each individual error, and saving iteration budget to solve different errors, if present.

The contrast between existing debugger and enhanced Agent is more clear when handling complex, interconnected changes. For instance, in updating Springfox Swagger from 2.0 to 3.0 (OpenAPI), both systems initially made similar changes. However, when faced with subsequent errors, their approaches diverged significantly. Consider this scenario:
Initially, both systems removed Springfox dependencies:

<!-- Removed by both systems -->
<dependency>
    <groupId>io.springfox</groupId>
    <artifactId>springfox-swagger2</artifactId>
    <version>2.9.2</version>
</dependency>

Later, when encountering a “missing symbol: Docket” error, existing debugger attempted to reintroduce Springfox:

<!-- existing debugger trying to add back Springfox -->
<dependency>
    <groupId>io.springfox</groupId>
    <artifactId>springfox-boot-starter</artifactId>
    <version>3.0.0</version>
</dependency>

In contrast, our Agent recognized this as consistent with the previous removal and rewrote the file using SpringDoc OpenAPI:

import org.springdoc.core.GroupedOpenApi;

@Configuration
public class SwaggerConfig {
    @Bean
    public GroupedOpenApi publicApi() {
        return GroupedOpenApi.builder()
                .group("springshop-public")
                .pathsToMatch("/public/**")
                .build();
    }
}

These latest improvements in our debug AI have yielded positive results. By incorporating multi-error context analysis, additional tooling of multi-file solution, and inter-iteration memory, the agent now delivers more comprehensive and consistent codebase upgrades. We tested our new approach on 62 large open-source applications, some containing over 100,000 lines of code, incorporating more than 100 open-source libraries. The results showed an 85% higher success rate compared to the previous approach. These enhancements significantly boost both the quality and efficiency of code transformation, marking a substantial leap forward in automated application modernization for Java.

Conclusion

With the latest improvements, Q Developer continues to accelerate the journey to modernize Java applications across your organization. For more context, please refer to the blog “Accelerate application upgrades with Amazon Q Developer agent for code transformation.”

As we continue to innovate in code transformation use cases, this release creates the foundation to expand language support, further enhance AI-driven problem-solving algorithms, and streamlining the integration with development workflows. Our goal remains to provide developers and organizations with cutting-edge tools that simplify complex maintenance and modernization processes and foster the adoption of modern, cloud-native architectures. Stay tuned for future updates as we push the boundaries of AI-assisted code transformation.

About the authors

Omer Tripp

Omer heads the Q Code Transformation science team. His research work is at the intersection of programming languages and AI/ML, emphasizing developer productivity and acceleration as well as software security and reliability. Outside of work, Omer likes to stay physically active (through tennis, basketball, skiing, and various other activities), as well as tour the US and the world with his family.

Jonathan Vogel

Jonathan is a Developer Advocate at AWS. He was a DevOps Specialist Solutions Architect at AWS for two years prior to taking on the Developer Advocate role. Prior to AWS, he practiced professional software development for over a decade. Jonathan enjoys music, birding and climbing rocks.

Yiyi Guo

Yiyi is a Senior Product Manager at AWS working on Amazon Q developer agent for code transformation, she focuses on leveraging generative AI to accelerate enterprise application modernization.

Elio Damaggio

Elio Damaggio is the product lead for the transformation capabilities of Amazon Q Developer. With more than 15 years in tech, 11 patents, and a PhD in Computer Science, he is now looking for exciting ways to empower developers through AI.

Special thanks to the scientists on the Q Developer team who helped to provide input to this blog: Talha Oz and Zeren Shui.

Your DevOps and Developer Productivity guide to 2024 re:Invent

Post Syndicated from Artur Rodrigues original https://aws.amazon.com/blogs/devops/your-devops-and-developer-productivity-guide-to-2024-reinvent/

It’s that time of the year again. The annual AWS re:Invent conference is just around the corner. Still need to save your spot? You can register here.

This year’s DevOps and Developer Productivity (DOP) track features an impressive lineup, including 11 breakout sessions, 14 chalk talks, 2 code talks, 8 workshops, 3 builder sessions, and 2 lightning talks.

I have curated a list of the DOP sessions that you should pay attention. I also invite you to visit the re:Invent catalog to explore the full range of DOP offerings. There is a collection of GenAI related sessions, leveraging Amazon Q Developer and Amazon Bedrock, as well as the usual AWS DevOps tools that we all love, Infrastructure as Code (IaC), Continuous Integration and Continuous Deployment (CI/CD).

How to reserve a seat in the sessions

Reserved seating is available for registered attendees to secure seats in the sessions of their choice. Reserve a seat by signing in to the attendee portal and navigating to “Event”, then “Sessions”.

Do not miss the Innovation Talk led by VP of Developer Experience, Adam Seligman. In DOP220 – Reimagining the Developer Experience at AWS – Software development is undergoing a seismic shift driven by generative AI, transforming how developers work, what they build, and who can become a developer. AWS empowers developers to fearlessly embrace this evolution, integrating cutting-edge yet responsible generative AI solutions across the development lifecycle. Explore real-world use cases accelerating legacy modernization, elevating cloud-native innovation, and unlocking remarkable results. Gain insights into AWS’s pragmatic approach, fueling creativity and customer impact. Join the vibrant community on this transformative journey, where generative AI is redefining software development, opening new frontiers for innovation, and democratizing access to coding for diverse creators shaping technology’s future.

DevOps and Developer Productivity breakout sessions

What are breakout sessions?

AWS re:Invent breakout sessions are lecture-style and 60 minutes long. These sessions are delivered by AWS experts and typically reserve 10–15 minutes for Q&A at the end. Breakout sessions are recorded and made available on-demand after the event.

DOP201 – AWS infrastructure as code: A year in review – AWS provides services that enable the creation, deployment and maintenance of application infrastructure in a programmatic, descriptive, and declarative way. These services provide rigor, clarity, and reliability to application development. Join this session to learn about the new features and improvements for AWS infrastructure as code with AWS CloudFormation and AWS Cloud Development Kit (AWS CDK) and discover how they can benefit your team.

DOP202 – Continuous integration and continuous delivery (CI/CD) for AWS – AWS provides one place where you can plan work, collaborate on code, and build, test, and deploy applications with continuous integration and continuous delivery (CI/CD) tools. In this session, learn about creating complete CI/CD pipelines using infrastructure as code on AWS.

DOP204 – Amazon Q Developer: Your gen AI assistant for software development – In this session, learn how Amazon Q Developer is transforming the developer experience by speeding up a range of tasks that support you as you research how to get started, evaluate system design, build secure and scalable applications, upgrade existing applications, and optimize application performance. Learn firsthand how Amazon Q capabilities for building, troubleshooting, and transforming applications faster and more easily frees you up to focus on experimentation and innovation.

DOP209 – Accelerate application maintenance and upgrades with generative AI – Developers spend significant time completing the undifferentiated work of maintaining and upgrading legacy applications. Teams need to balance investments in building new features with mandatory patching and update work. Now, using the power of generative AI, the Amazon Q Developer agent for code transformation can expedite these critical upgrade tasks, transforming applications to use the latest language features and versions in hours or days and saving significant costs. Join the session to learn what’s new and how your team can automate Java application upgrades.

DOP214 – Unleashing generative AI: Amazon’s journey with Amazon Q Developer – Join us to discover how Amazon rolled out Amazon Q Developer to thousands of developers, trained them in prompt engineering, and measured its transformative impact on productivity. In this session, learn best practices for effectively adopting generative AI in your organization. Gain insights into training strategies, productivity metrics, and real-world use cases to empower your developers to harness the full potential of this game-changing technology. Don’t miss this opportunity to stay ahead of the curve and drive innovation within your team.

DevOps and Developer Productivity chalk talks

What are chalk talks?

Chalk Talks are highly interactive sessions with a small audience. Experts lead you through problems and solutions on a digital whiteboard as the discussion unfolds. Each begins with a short lecture (10–15 minutes) delivered by an AWS expert, followed by a 45- or 50-minute Q&A session with the audience.

DOP318 – Prompt engineering expertise: Unleashing code with Amazon Q Developer – Dive into the art of prompt engineering and discover how to harness the full potential of Amazon Q Developer, AWS’s cutting-edge generative AI service. Learn techniques to craft compelling prompts that yield remarkable code generation results. Explore strategies to provide contextual information beyond prompts, such as import statements, to enhance the accuracy and relevance of your AI-generated code. Elevate your software development workflow and unleash the transformative capabilities of generative AI.

DOP324 – Incorporating generative AI in the CI/CD pipeline – In this chalk talk, discover how generative AI can revolutionize your continuous integration and delivery (CI/CD) pipeline. Learn how AI models can analyze code changes and generate recommendations for safe deployments. Explore automated orchestration capabilities that trigger deployments, monitor metrics, and adapt strategies. Gain insights into using AI for continual monitoring and self-improving release cycles, streamlining your software delivery while minimizing risks and manual efforts.

DOP314 – Automate Java app upgrades & accelerate innovation with generative AI – Amazon Q Developer’s agent for code transformation automates the end-to-end process of upgrading and transforming code. Reduce the time and costs associated with modernizing applications, unlock previously cost-prohibitive and cumbersome modernization opportunities, and save customers months or even years of effort. By automating undifferentiated upgrade and modernization tasks, customers can enhance application performance and security and accelerate innovation. Join this chalk talk to learn how to take your application modernization to the next level.

DOP323 – From Windows to Linux: .NET application modernization – Porting and upgrades of .NET applications running on Windows servers to Linux can deliver cost savings and enhance security and compliance, but the modernization process can be long and laborious. This interactive chalk talk explores strategies for porting server-side components of a .NET application within days by refactoring. The session includes codebase analysis, code decomposition into buildable units, transformation plan creation, and execution of key transformation tasks with approval from the developer.

DevOps and Developer Productivity workshops

What are workshops?

Workshops are two-hour interactive learning sessions where you work in small group teams to solve problems using AWS services. Each workshop starts with a short lecture (10–15 minutes) by the main speaker, and the rest of the time is spent working as a group.

DOP304 – Develop AWS CDK resources to deploy your applications on AWS – In this workshop, learn how to build and deploy applications using infrastructure as code with AWS Cloud Development Kit (AWS CDK). Create resources using AWS CDK, and learn maintenance and operations tips. In addition, get an introduction to building your own constructs. You must bring your laptop to participate.

DOP305 – Modern CI/CD with GitHub and AWS CodePipeline – In this workshop, learn how to build modern continuous integration and continuous delivery (CI/CD) pipelines using GitHub and AWS CodePipeline through the AWS Management Console. Learn how to work with monorepos and branching strategies. Explore advanced features such as automatic rollbacks, pipeline parameters, stage level conditions, and concurrent execution modes to improve your pipeline performance. You must bring your laptop to participate.

DOP309 – The Amazon Q Developer coding challenge – Join this workshop to participate in 20 increasingly complex coding challenges aided by Amazon Q Developer, an AI-powered assistant for software development. Discover how Amazon Q Developer’s auto-generated code recommendations and chat explanations can help you develop code and understand complex algorithmic coding challenges more efficiently compared to manual coding alone. Learn about Amazon Q Developer capabilities and how it can help you improve productivity. You must bring your laptop to participate.

DOP325 – Boost code quality with generative AI – In this hands-on workshop, you unleash the power of generative AI to boost code quality using Amazon Q Developer. You learn to use Amazon Q Developer to generate unit tests and documentation automatically, addressing the challenge of balancing new feature development with writing unit tests and documentation. By the end of the workshop, you have firsthand experience streamlining your development process and freeing up time to focus on core feature development. Come follow along with step-by-step instructions and gain practical experience with this cutting-edge AWS service. You must bring your laptop to participate.

DevOps and Developer Productivity builders’ sessions

What are builders’ sessions?

These 60-minute group sessions are led by an AWS expert and provide an interactive learning experience for building on AWS. Builders’ sessions are designed to create a hands-on experience where questions are encouraged.

DOP205 – Learning new skills with Amazon Q Developer – Experience the power of Amazon Q Developer, your AI-powered assistant for software development. In this session, explore how Amazon Q Developer can streamline your daily workflow on AWS. Stuck in the console? Open the Amazon Q Developer panel for instant assistance. Can’t find your way through the documentation? Amazon Q Developer guides you effortlessly. Need help crafting CLI commands? Amazon Q Developer has you covered. Want assistance right in Slack or Microsoft Teams? Amazon Q Developer is by your side, helping you work smarter, faster, and more efficiently across your favorite tools. You must bring your laptop to participate.

DOP302 – Creating secure code with Amazon Q Developer – In this builders’ session, gain hands-on experience using Amazon Q Developer to create secure code. Write unit tests, optimize code, and scan for vulnerabilities, and discover how Amazon Q Developer suggests remediations that help fix your code instantaneously. Also, learn how you can use Amazon Q Developer security scanning to outperform other publicly benchmarkable tools on detection across popular programming languages. You must bring your laptop to participate.

DOP401 – Modernizing Java applications with Amazon Q Developer – In this builders’ session, use Amazon Q Developer Agent for code transformation to modernize a Java application. Learn how Amazon Q Developer can leverage generative AI to automate common language upgrade tasks like updating your code, conducting unit tests, and verifying deployment readiness starting with Java. Save days’ or even months’ worth of the undifferentiated work involved in moving from older language versions. You must bring your laptop to participate.

DevOps and Developer Productivity lightning talks

What are lightning talks?

Lightning talks are short, 20-minute demos led from a stage.

DOP217 – Best practices for customizing Amazon Q Developer – With Amazon Q Developer, you can securely connect to your private repositories to generate even more relevant code recommendations based on your internal code repositories, ask questions about your company code, and understand your internal code bases faster. In this session, learn how to set up customizations and generate code based on your internal repos. Use the Amazon Q Developer chat in your IDE to ask questions about how your internal code base is structured, where and how certain functions or libraries are used, and how to use specific functions, methods, or APIs.

DOP219 – How NAB uses Amazon Q Developer for increased productivity – Significantly accelerate development by customizing Amazon Q Developer to generate even more relevant inline code recommendations and chat responses (in preview) by making it aware of your internal libraries, APIs, best practices, and architectural patterns. In this lightning talk, you learn how National Australia Bank (NAB) is using Amazon Q Developer to help their development teams ship faster, and innovate more for their customers, by using customizations.

DevOps and Developer Productivity code talks

What are code talks?

Code talks are 60-minute, highly-interactive discussions featuring live coding. Attendees are encouraged to dig in and ask questions about the speaker’s approach.

DOP313 – Get tailored code insights with Amazon Q Developer and private repos – Unlock the full potential of Amazon Q Developer with customized code recommendations tailored to your organization’s code base. In this code talk, learn how to securely connect Amazon Q to your private repositories, enabling it to generate highly relevant code suggestions based on your internal coding practices. Discover how to create and utilize customizations, and witness firsthand the transformative impact on code comprehension and development efficiency by comparing suggestions with and without customization. Elevate your coding experience with this powerful feature.

DOP315 – Optimize your cloud environments in the AWS console with generative AI – Available in the AWS Management Console, Amazon Q Developer is the only AI assistant that is an expert on AWS, helping developers and IT pros optimize their AWS cloud environments. Proactively diagnose and resolve errors and networking issues, provide guidance on architectural best practices, analyze billing information and trends, and use natural language in chat to manage resources in your AWS account. Learn how Amazon Q Developer accelerates task completion with tailored recommendations based on your specific AWS workloads, shifting from a reactive review to proactive notifications and remediation.

Want to stay connected?

Get the latest updates for DevOps and Developer Productivity by following us on Twitter and visiting the AWS devops blog.

If you are unable to join us in-person, Breakout Sessions will be available via our YouTube channel after the event. Contact your AWS Account Team is you are interested in learning more about any of these sessions or how to bring our experts to you.

We look forward to seeing you at re:Invent 2024!

Artur Rodrigues

Artur Rodrigues is a Principal Solutions Architect for Generative AI at Amazon Web Services (AWS), where he empowers developers to leverage cutting-edge AI technologies to enhance their workflows and drive innovation. As a co-founder of the University of British Columbia Cloud Innovation Center (UBC-CIC), powered by AWS, Artur has collaborated with researchers, physicians, and students to develop over 50 solutions addressing real-world challenges. Artur enjoys cycling and exploring the great outdoors of beautiful British Columbia in Canada. He is also a gelato aficionado and a fan of soccer and jiu-jitsu.

Expanded resource awareness in Amazon Q Developer

Post Syndicated from Brendan Jenkins original https://aws.amazon.com/blogs/devops/expanded-resource-awareness-in-amazon-q-developer/

Recently, Amazon Q Developer announced expanded support for account resource awareness with Amazon Q in the AWS Management Console along with the general availability of Amazon Q Developer in AWS Chatbot, enabling you to ask questions from Microsoft Teams or Slack. Additionally, Amazon Q will now provide context-aware assistance for your questions about resources in your account depending on where you are in the console. Amazon Q in the console gives you the ability to use natural language with the Amazon Q Developer chat capability to list resources in your AWS account, get specific resource details, and ask about related resources, launched in preview on April 30, 2024.

In this blog, I will highlight the new expanded functionality of this feature in Amazon Q Developer including understanding relationships between account resources, context-awareness, and the general availability of the AWS Chatbot integration with Microsoft Teams and Slack.

Expanded account resource awareness with Amazon Q Developer

Prior to the launch of the expanded support, you could ask Amazon Q Developer to list resources in your AWS Account with prompts such as “List all my EC2 instances in us-east-1” and the service would list all your Amazon Elastic Compute Cloud (Amazon EC2) instances. Now, with the expanded support, you can ask more complex questions about your AWS account resources. I will show a few examples in this section of this post.

For our first example, imagine that you’re a developer who is responsible for maintaining code as a part of the software development lifecycle (SDLC) and you frequently use AWS Lambda for development and Amazon Relational Database Service (RDS) in the backend as a part of your development process. With this new update, a developer could open a new Q chat in the AWS Management Console, and enter a prompt such as: “Which RDS clusters are due for an update?”

User entering prompt Amazon Q Developer chat in the AWS management console about listing all RDS clusters that need updates in their account and Amazon Q listing those Databases.

Figure 1: Amazon Q Developer listing RDS clusters needing an update

As a result, the Amazon Q Developer console chat will return a list of all your Amazon RDS clusters that have available updates as shown in Figure 1 above.

Now, for another example, you want to update any Lambda functions in your AWS account that had a Simple Notification Service (SNS) topic as a trigger due to moving to a new SNS topic you recently created. To identify which SNS topics are still being used, you could enter a prompt such as “List all the SNS topics that trigger a lambda function.”

User entering prompt Amazon Q Developer chat in the AWS management console about listing all SNS topics that trigger a lambda function and Amazon Q listing the SNS topics as an output.

Figure 2: Amazon Q listing SNS topics that are lambda triggers

As shown in the prior example, Amazon Q Developer was able to identify any SNS topics in the form of Amazon resource name (ARN) that was set to trigger a lambda function in the AWS account as intended.

Additionally, you can ask a follow up question in the same chat to investigate more. You can send a prompt such as “Which lambda function uses the arn:aws:sns:us-east-1:76859XXXX:FailoverHealthcheck SNS topic?”

User entering prompting Amazon Q Developer chat with a follow up question in the AWS management console about which Lambda is associated with an SNS topic.

Figure 3: Asking Q Developer a follow up question about a resource

From Figure 3 above, you can see that there is a Lambda function/endpoint associated with the SNS topic resource that Amazon Q Developer was able to identify.

Outside of the examples above, here are some other prompts/examples that can be explored for the expanded support:

– “Do I have any ECS clusters with pending tasks?”

– “Are there any ECS clusters in my account with services in DRAINING status?”

Amazon Q Developer understands where you are in the console

Amazon Q Developer in the AWS Management Console now provides context-aware assistance for your questions about resources in your account. This feature allows you to ask questions directly related to the console page you’re viewing, eliminating the need to specify the service or resource in your query. Q Developer uses the current page as additional context to provide more accurate and relevant responses, streamlining your interaction with AWS services and resources.

Prior to the update, a user would have to prompt, “What is the public IPv4 address of my instance i-08ccXXXXXX?”  Now, if you are viewing an EC2 instance in the console and prompt Amazon Q, “What is the public IPv4 address of my instance?” you will not need to specify the instance you are referring to.

User entering prompt Amazon Q Developer chat in the AWS management console about what the IP address is of the instance on the page.

Figure 4: Asking Amazon Q about an EC2 instance being viewed

In figure 4 above, Amazon Q’s console chat was able to use its context-awareness to pick up on what the IPv4 address was on the console page where I was currently working, despite me not specifying which instance I was referring to.

AWS ChatBot can now answer questions about AWS resources in Microsoft Teams and Slack

Recently, we announced the general availability of Amazon Q Developer in AWS Chatbot, which provides answers to customers’ AWS resource related queries in Microsoft Teams and Slack. This gives teams the ability to quickly find relevant resources to troubleshoot issues using natural language queries in the chat channels of Microsoft Teams or Slack.

For example, you could integrate the AWS Chatbot Service with Amazon Q Developer to allow you to enter a prompt in Slack such as “@aws show EC2 instances in running state in us-east-1”.

User entering prompt in slack to ask the AWS Chatbot about EC2 resources and Amazon Q responding

Figure 5: Amazon Q listing all EC2 resources in Slack

As shown in figure 5 above, Amazon Q was able to list all the EC2 resources and place them into a slack channel showing an example of the functionality in action.

Conclusion

Amazon Q Developer has enhanced its cloud resource management capabilities, enabling more intuitive and intelligent interactions with AWS resources. The new features allow developers to ask complex, context-aware questions about their cloud infrastructure directly through the AWS Management Console, Microsoft Teams, and Slack. Users can now easily discover new details about specific resources with natural language queries that provide precise, contextual information. These improvements represent a significant step forward in simplifying cloud resource management, making it faster and more user-friendly for development teams to understand, track, and maintain their AWS environments. To learn more about chatting with your AWS resources, check out Console documentation and AWS Chatbot documentation.

About the authors

Brendan Jenkins

Brendan Jenkins is a Tech Lead Solutions Architect at Amazon Web Services (AWS) working with Enterprise AWS customers providing them with technical guidance and helping achieve their business goals. He has an area of specialization in DevOps and Machine Learning technology.

Amazon Q Developer plugins now generally available for the AWS Management Console

Post Syndicated from Shardul Vaidya original https://aws.amazon.com/blogs/devops/amazon-q-developer-plugins-now-generally-available/

Today, Amazon Web Services (AWS) announced the launch and general availability of Amazon Q Developer plugins for Datadog and Wiz in the AWS Management Console. When chatting with Amazon Q in the console, customers can access a subset of information from Datadog and Wiz services using natural language. Ask questions like @datadog do I have any active alerts? or @wiz what are my top 3 security issues today? to swiftly identify and fix problems without leaving the console.

Engineers and IT professionals can struggle with tool sprawl throughout an application’s operational lifecycle. Amazon Q Developer’s third-party plugin system works towards creating a single pane of glass for all your SaaS solutions.

In this post, we’ll explore:

  • How Q Developer plugins work
  • How to use these plugins to:
    • Understand the state of your infrastructure
    • Query and brainstorm on present issues
    • Generate code and CLI commands to use third-party systems
  • How to get started

Our goal is for you to gain a comprehensive understanding of how the third-party plugins will improve your operational productivity.

How do Q Developer plugins work

Amazon Q in the console uses the prefix you provide to select which plugin to query. This provides additional context on your request and the state of your infrastructure. Key processes include:

  • Intent recognition: Amazon Q Developer interprets your chat request’s intent. It searches through relevant APIs it can invoke and selects the correct workflow to get more context.
  • API invocation: Amazon Q Developer then calls the appropriate third-party APIs to gather relevant information. Neither the AWS context included in the chat nor any information from your prompt is passed to the third-party.
  • Response Generation: After obtaining the enriched context and original prompt, Amazon Q Developer composes a complete prompt. Amazon Q uses this to generate the best response.
  • Guardrails: The system checks the response against Amazon Q Developer guardrails to ensure it follows best practices.

This system enables Amazon Q Developer to, understand intent, request additional information, and provide rich assistance across your infrastructure and application operations.

Let’s see how each of the third-party plugins can help in a set of real-world use-cases.

Amazon Q Developer plugin for Datadog

Datadog, an AWS Advanced Technology Partner and observability and security platform for cloud applications, provides AWS customers with unified, real-time observability and security across their entire technology stack. Datadog unifies all of your telemetry in one place, so teams can troubleshoot, optimize, and secure resources at scale. If you use Datadog to
monitor your AWS infrastructure and applications, you can query a subset of information from Datadog without leaving the AWS console by prefixing your Amazon Q queries with @datadog.

Learn to use Datadog in your workloads

You can ask about how Datadog features work with certain AWS services, by asking questions like @datadog how do I use APM on my EC2 instance?
Gif of Q Developer plugin for Datadog answering a question about how to use APM on EC2

Retrieve and summarize cases and monitors

You can ask about specific cases, monitors, or specify properties of a case to get more information about it and include it in your conversation by asking questions like @datadog list my cases. With a follow up to quickly get a summary of your top cases, @datadog summarize my top cases

Gif of Q Developer plugin for Datadog answering a question about all the current cases in the connected instance of datadogGif of Q Developer plugin for Datadog answering a question summarizing the top cases in the connected instance of datadog

Check and list monitors in alarm

You can ask about specific application monitors as well, including which monitors are in alarm, Amazon Q Developer also allows follow-up questions about which alarmed monitors. You can start with a question like, @datadog list my current monitors

Gif of Q Developer plugin for Datadog listing out all the monitors in the connected instance of datadogGif of Q Developer plugin for Datadog stating that there are currently no monitors in an alarmed state in the connected instance of Datadog

And then follow it up with a question like, @datadog List some of the resources that are triggering the alarm

Amazon Q Developer plugin for Wiz

With Wiz, organizations can democratize security across the development lifecycle, empowering them to build fast and securely. As an AWS Security Competency Partner, Wiz is committed to effectively reducing risk for AWS customers by seamlessly integrating into AWS services. If you use Wiz to monitor your AWS infrastructure and applications, then you can query Wiz without leaving the console by prefixing your queries with @wiz.

View issues with critical severity

You can ask Q Developer to retrieve the specifics of your issues in Wiz, the plugin can currently return up to 10 issues and you can focus on a specific severity with a question like, @wiz list the issues with critical severity
With that response, we can also ask it to find the top issues, with a follow-up question like, @wiz can you specify the top 5?
Gif of Q Developer plugin for Wiz showing how many critical severity issues detected by the connected instance of Wiz

Find your critical resources

Wiz defines the security posture of your AWS resources based on their configuration and how many critical issues that are associated with them. Amazon Q Developer can ask Wiz which are the least secure resources with a question like, @wiz what are the critical resources in my AWS environment?
Gif of Q Developer plugin for Wiz listing out all the critical resources noted by the connected Wiz instance

List issues based on certain properties

Wiz tracks security issues that exist in your AWS account and you can ask Amazon Q Developer to list issues based on date, status, severity or type, with questions like, @wiz what issues are due next?
Gif of Q Developer plugin for Wiz listing the next few issues listed in the connected Wiz instance

Assess issues with security vulnerabilities

Wiz tracks external vulnerabilities and exposures that can potentially pose a security threat associated with your current resources and issues. Amazon Q Developer can ask Wiz which are the pertinent vulnerabilities with a question like, @wiz what are my issues that have been created in the last 7 days?
Gif of Q Developer plugin for Wiz listing the issues that Wiz lists are newest

Getting Started

To enable third-party Plugin capability in the Amazon Q Developer console:

  1. To use third-party plugins, subscribe to Amazon Q Developer Pro Tier if you don’t already have it. This activates plugins at an organizational level.
  2. If you don’t already have a Amazon Q Administrator Role/User, create one using either the AmazonQFullAccess / AmazonQDeveloperAccess managed policies, or follow the instructions in the Q Developer user guide for security and IAM permissions.
  3. Configure the plugins – To activate the plugins, you must configure their credentials to authenticate into the third-party system. This is possible through a new tab called “Plugins” in the Amazon Q Developer dashboard. The plugins require credentials from the third parties to authenticate and call APIs specific to your accounts. They’re stored in your AWS account in Secrets Manager.
    Image of the Amazon Q Developer dashboard in the AWS Management Console showing the new Plugin sidebar item

    1. Datadog – Follow the instructions in the Datadog API documentation to create a Datadog API key and copy over the Site URL, API Key, and application key to authorize Q Developer with your instance of Datadog.
      Image of the Amazon Q Developer dashboard showing the configuration screen for the Datadog plugin requesting the Site URL, API key, and application key for the instance of Datadog you wish to connect Image of the Datadog settings UI showing where to get the Site URL, API key, and application key
    2. Wiz – Follow the instructions in the Wiz Service account documentation to create a client ID, the client secret generated by wiz, and then retrieve the Wiz API endpoint URL to connect Amazon Q Developer to Wiz.
      Image of the Amazon Q Developer dashboard showing the configuration screen for the Wiz plugin; requesting the client ID, client secret, and the Wiz API endpoint URL for the instance you wish to connectImage of the Wiz UI settings UI showing where to get the client ID, client secret, and the API endpoint URL
  4. Query the new plugins – With the @datadog and @wiz prefixes, you can ask a wide variety of questions and get operational assistance leveraging from third-party SaaS products. This allows you to integrate data from all sources with lower overhead and friction.
  5. Iterate and refine – Try rephrasing or explicitly including more context about the request by mentioning dates or issue severity. Providing more relevant information helps Amazon Q Developer better understand your request.

For best results with third-party plugins, understand what you’re looking for and use terminology specfic to the third-party. Avoid overly broad queries to guide Amazon Q Developer effectively.

Conclusion

In this post, we introduced Amazon Q Developer’s third-party plugins in chat via the @datadog and @wiz prefixes highlighting the benefits of using plugins when trying to leverage generative AI across multiple services. By allowing Q Developer to understand and analyze the state of your infrastructure across services, third-party plugins unlock new boundaries for operational productivity gains.

Shardul Vaidya is a Worldwide Partner Solutions Architect with AWS, focused on helping partners and customers build and effectively use Generative AI powered developer experiences. Shardul joined AWS in 2020 as part of their early career talent Solutions Architect team and worked with over a hundred modernization and DevOps partners across the world. Outside of work, he’s a music lover and collects records.

AWS Weekly Roundup: 20 years of AWS News Blog, Express brokers for Amazon MSK, Windows Server 2025 images on EC2, and more (Nov 11, 2024)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-20-years-of-aws-news-blog-express-brokers-for-amazon-msk-windows-server-2025-images-on-ec2-and-more-nov-11-2024/

Happy 20th Anniversary of the AWS News Blog! 🎉🥳🎊 On November 9, 2004, Jeff Barr published his first blog post. At the time, he started a personal blog site using TypePad. He wanted to speak to his readers with his personal voice, not the company or team.

On April 29, 2014, we created a new AWS blog site and migrated all posts to that page. There are currently over 4,300 posts on the AWS News Blog, with Jeff contributing over 3,200 of them.

Since December 2016, the AWS News Blog has added new writers, but we are still following Jeff’s leadership principals for AWS News Bloggers in accordance with Day One. What’s unique about the AWS News Blog is that the blog writers get to use the features of the product team in advance, following the Customer Obsession leadership principle, and focus on walk-throughs of how customers can quickly use them to save time, with the Frugality principle.

I am very grateful for Jeff’s fundamental and pivotal role over the past 20 years, and I look forward to the next 20 years!

Last week’s launches
Here are some launches that got my attention:

New Express brokers for Amazon MSK – Express brokers are a new broker type for Amazon MSK Provisioned designed to deliver up to three times more throughput per broker, scale up to 20 times faster, and reduce recovery time by 90 percent as compared to standard Apache Kafka brokers. Express brokers come preconfigured with Kafka best practices by default, support all Kafka APIs, and provide the same low-latency performance, so you can continue using existing client applications without any changes.

New Amazon Kinesis Client Library 3.0 – You can now reduce compute costs to process streaming data by up to 33 percent with Kinesis Client Library (KCL) 3.0, compared to previous KCL versions. KCL 3.0 introduces an enhanced load balancing algorithm that continuously monitors resource utilization of the stream processing workers and automatically redistributes the load from overutilized workers to other underutilized workers. To learn more, read the AWS Big Data Blog post.

Microsoft Windows Server 2025 images on Amazon EC2 – We now support Microsoft Windows Server 2025 with License Included (LI) Amazon Machine Images (AMIs), providing customers with an easy and flexible way to launch the latest version of Windows Server. By running Windows Server 2025 on Amazon EC2, customers can take advantage of the security, performance, and reliability of AWS with the latest Windows Server features. To learn more about running Windows Server 2025 on Amazon EC2, visit Windows Workloads on AWS.

Anthropic’s Claude 3.5 Haiku model in Amazon Bedrock – Claude 3.5 Haiku is the next generation of Anthropic’s fastest model, combining rapid response times with improved reasoning capabilities, making it ideal for tasks that require both speed and intelligence. Claude 3.5 Haiku improves across every skill set and surpasses even Claude 3 Opus, the largest model in Anthropic’s previous generation, on many intelligence benchmarks—including coding. To learn more, read the AWS News Blog post.

Amazon Bedrock Prompt Management GA – You can simplify the creation, testing, versioning, and sharing of prompts in Amazon Bedrock Prompt Management. At general availability, we added new features that provide enhanced options for configuring your prompts and enabling seamless integration for invoking them in your generative AI applications, such as structured prompts and Converse and InvokeModel API integration. To learn more, read the AWS Machine Learning blog post.

Six new synthetic generative voices for Amazon Polly – The generative engine is Amazon Polly’s most advanced text-to-speech (TTS) model leveraging the generative AI technology. We added six new synthetic female-sounding generative voices: Ayanda (South African English), Léa (French), Lucia (European Spanish), Lupe (American Spanish), Mía (Mexican Spanish), and Vicki (German). This extends thirteen voices and nine locales to provide you with more options of highly expressive and engaging voices.

Amazon OpenSearch Service Extended Support – We announce the end of Standard Support and Extended Support timelines for legacy Elasticsearch versions and OpenSearch Versions. Standard Support ends on Nov 7, 2025, for legacy Elasticsearch versions up to 6.7, Elasticsearch versions 7.1 through 7.8, OpenSearch versions from 1.0 through 1.2, and OpenSearch versions 2.3 through 2.9. With Extended Support, for an incremental flat fee over regular instance pricing, you continue to get critical security updates beyond the end of Standard Support. To learn more, read the AWS Big Data Blog post.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional news items that you might find interesting:

CEO’s visiting at AWS data center – Matt Garman, CEO of AWS, had a great time visiting one of our AWS data centers recently, and was able to get a look at the continuous innovation delivered by the team. Of course, it’s no surprise that Amazon’s senior executives visit fulfillment centers, contact centers, and data centers, to do real work for customers. AWS data centers are designed for customers in every aspect, for maximum resilience, performance, and energy efficiency.

AWS supports small businesses, creates jobs, sets up sustainability initiatives, and develops educational programs near AWS data centers. Get the latest updates – AWS in your community: Here’s what’s happening near data centers across the US on About Amazon News.

Amazon Q Business at Amazon – I introduced an Amazon story to use Code transformation in Amazon Q Developer to migrate more than old 30,000 Java applications to Java 17 version. It saved over 4,500 developer years of effort compared to previous manual jobs and saved the company $260 million in annual by moving to the latest Java version.

Here is another dogfooding story of Amazon Q Business at Amazon. Amazon built an internal chatbot with Amazon Q Business and it has resolved over 1 million internal Amazon developer questions, reducing time spent churning on manual technical investigations by more than 450,000 hours.

Our team onboarded Amazon Q Business with millions of internal documents and integrated Q Business into the tools our team use every day. Now, instead of waiting hours for responses to complex technical questions on Q&A boards or Slack channels, developers can get answers in seconds.

TOURCast at PGA TOUR – If you enjoy golf, this news will be of interest to you. The PGA TOUR debuted TOURCast in Japan at the 2024 ZOZO Championship to capture and disseminate better statistical data and bring fans closer to the game based on new scoring system called ShotLink, powered by CDW. This marks the first time the PGA TOUR has been able to bring this technology to Asia, leveraging the flexibility and scalability of AWS to overcome unique challenges.


PGA TOUR volunteer setting up GPS equipment on the fairway at ZOZO championship that will input specific shot data and feed back to Shotlink Select Plus. [IMAGE: PGA TOUR]

They’ve completely rebuilt their scoring system over the past two years on a new cloud stack. With AWS cloud, whether data comes from high-tech radar systems, cameras, or manual input, the system processes it all seamlessly.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS GenAI Lofts – AWS GenAI Lofts are about more than just the tech, they bring together startups, developers, investors, and industry experts. Whether you’re looking to gain deep insights, or get your questions answered by generative AI pros, our GenAI Lofts have you covered, and provide everything you need to start building your next innovation. Join events in São Paulo (through November 20), and Paris (through November 25).

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Jakarta, Indonesia (November 23), Kochi, India (December 14).

AWS re:Invent – You can still register for the annual learning event, taking place December 2–6 in Las Vegas. Surprisingly Andy Jassy, CEO of Amazon said he will come back and participate in AWS re:Invent this year. He said “As always, the priority is to make this a learning event so customers can take nuggets back and change their own customer experiences and businesses. We’ll also have a bunch of goodies for you that we’ll announce and that we think folks will like.” Let’s meet there!

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Weekly Roundup: AWS Lambda, Amazon Bedrock, Amazon Redshift, Amazon CloudWatch, and more (Nov 4, 2024)

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-lambda-amazon-bedrock-amazon-redshift-amazon-cloudwatch-and-more-nov-4-2024/

The spooky season has come and gone now. While there aren’t any Halloween-themed releases, AWS has celebrated it in big style by having a plethora of exciting releases last week! I think it’s safe to say that we have truly entered the ‘pre’ re:Invent stage as more and more interesting things are being released every week on the countdown to AWS re:Invent 2024.

There is a lot to cover, so let me put my wizard hat on, open the big bag of treats, and dive into last week’s goodies!

Something for developers
There was no shortage of treats from AWS for developers this Halloween!

AWS enhances the Lambda application building experience with VS Code IDE and AWS Toolkit — AWS has enhanced AWS Lambda development with the AWS Toolkit for Visual Studio Code, providing a guided setup for coding, testing, and deploying Lambda applications directly within the IDE. It includes sample walkthroughs and one-click deployment, simplifying the development process. Now, building apps with Lambda is as intuitive as crafting a spell in a wizard’s workshop!

AWS Amplify integration with Amazon S3 for static website hosting — AWS Amplify Hosting now integrates with Amazon S3 for seamless static website hosting, with global CDN support via Amazon CloudFront. This simplifies set up, offering secure, high-performance delivery with custom domains and SSL certificates. Hosting your site is now easier than spotting a jack-o’-lantern on Halloween night!

AWS Lambda now supports AWS Fault Injection Service (FIS) actions — AWS Lambda now supports AWS Fault Injection Simulator (FIS) actions, enabling developers to test resilience by injecting controlled faults like latency and execution errors. This helps simulate real-world failures without code changes, improving monitoring and operational readiness. Great for testing that old candy dispenser!

AWS CodeBuild now supports retrying builds automatically — AWS CodeBuild now offers automatic build retries, allowing developers to set a retry limit for failed builds. This reduces manual intervention by automatically retrying builds up to the specified limit, tackling those pesky, intermittent failures like a ghostbuster clearing a haunted pipeline!

Amazon Virtual Private Cloud launches new security group sharing features — Amazon VPC now supports sharing security groups across multiple VPCs within the same account and with participant accounts in shared VPCs. This streamlines security management and ensures consistent traffic filtering across your organization. Now, keeping your network secure is as seamless as warding off digital goblins!

Amazon DataZone expands data access with tools like Tableau, Power BI, and more — Amazon DataZone now supports the Amazon Athena JDBC Driver, allowing seamless access to data lake assets from BI tools, like Tableau and Power BI. This lets analysts connect and analyze data with ease. Now, accessing data is as effortless as a witch flying on her broomstick!

Generative AI
Amazon Q and Amazon Bedrock continue to make generative AI seem like magic. Here are some releases from last week.

Amazon Q Developer inline chat — Amazon Q Developer has introduced inline chat support, allowing developers to engage directly within their code editor for actions like optimization, commenting, and test generation. Real-time inline diffs make it simple to review changes, available in Visual Studio Code and JetBrains IDEs. It’s practically code magic – no witch’s cauldron needed!

Meta’s Llama 3.1 8B and 70B models are now available for fine-tuning in Amazon Bedrock — Amazon Bedrock now supports fine-tuning for Meta’s Llama 3.1 8B and 70B models, allowing developers to customize these AI models with their own data. With a 128K context length, Llama 3.1 processes large text volumes efficiently, making it perfect for domain-specific applications. Now, your AI won’t be scared of handling monstrous amounts of data—even on a dark, stormy night!

Fine-tuning for Anthropic’s Claude 3 Haiku in Amazon Bedrock is now generally available — Fine-tuning for the Claude 3 Haiku model in Amazon Bedrock is now generally available, enabling customization with your data for better accuracy. Make your AI as unique as your Halloween costume!

Cost Planning, Saving, and Tracking
Here are some new releases that can help you stay on top of your budget and keep an eye on the amount of candy that you buy.

AWS now accepts partial card payments — AWS now supports partial payments with credit or debit cards, letting users split monthly bills across multiple cards. This flexibility makes managing your budget as smooth as a ghost gliding through a haunted house!

Amazon Bedrock now supports cost allocation tags on inference profiles — Amazon Bedrock now supports cost allocation tags for inference profiles, allowing customers to track and manage generative AI costs by department or application. This makes financial management a treat, not a trick!

AWS Deadline Cloud now adds budget-related events — AWS Deadline Cloud, a service used for rendering and managing visual effects and animation workloads, now sends budget-related events via Amazon EventBridge, enabling real-time spending updates and automated notifications. This helps keep project costs under control without any unexpected scares!

And the busiest team of the week award goes to…Amazon Redshift!
Clearly, the Amazon Redshift team loves Halloween and decided to celebrate in big style with many releases! Here are the highlights:

Amazon Redshift integration with Amazon Bedrock for generative AI — Amazon Redshift now integrates with Amazon Bedrock for generative AI tasks using SQL, adding AI capabilities like text generation directly in your data warehouse. Now, you can conjure up rich insights without the need for complicated spells!

Announcing general availability of auto-copy for Amazon Redshift — Auto-copy for continuous data ingestion from Amazon S3 into Amazon Redshift is now generally available. This streamlines workflows, making data integration as smooth as carving a soft pumpkin!

Amazon Redshift now supports incremental refresh on Materialized Views (MVs) for data lake tables — Amazon Redshift now supports incremental refresh for materialized views on data lake tables, updating only changed data to boost efficiency. This keeps your data fresh without any haunting overhead!

Announcing Amazon Redshift Serverless with AI-driven scaling and optimization — Amazon Redshift Serverless now offers AI-driven scaling, adjusting resources automatically based on workload. This ensures smooth performance without any chilling surprises!

CSV result format support for Amazon Redshift Data API — Amazon Redshift Data API now supports CSV output for SQL query results, enhancing data processing flexibility. This makes handling data as smooth as a ghost’s whisper!

Halloween week contest runner-up…Amazon CloudWatch!
The Amazon CloudWatch team has also been busy giving out candy this Halloween! Let’s check it out.

Amazon CloudWatch now monitors EBS volumes exceeding provisioned performance — Amazon CloudWatch now provides metrics to check if Amazon EBS volumes exceed their IOPS or throughput limits. This helps quickly spot and resolve performance issues before they turn into a haunting problem!

New Amazon CloudWatch metrics for monitoring I/O latency of Amazon EBS volumes — Amazon CloudWatch now offers metrics for average read and write I/O latency of Amazon EBS volumes, aiding in identifying performance issues. These insights are available per minute at no extra cost. This should help you prevent latency from sneaking up on you like a Halloween ghost!

Amazon ElastiCache for Valkey adds new CloudWatch metrics to monitor server-side response time — Amazon ElastiCache now includes metrics for read and write request latency, helping monitor server response times. This aids in quickly spotting and resolving performance issues before they become a frightful surprise!

Conclusion
And that’s a wrap on Halloween 2024. I don’t know about you, but this is my favorite time of the year, followed by News Year’s. Both carry an element of unpredictability that I very much enjoy. With Halloween, you can get excited about what costume you’re going to wear, whereas New Year’s is all about new possibilities and conquering new horizons.

Luckily, you don’t have to wait for the new year to unlock new frontiers with AWS as we bring excitement and innovation throughout the year. And what better way to see this in action than at AWS re:Invent 2024!

I wonder what kinds of spells and surprises we’ll be conjuring up come Halloween 2025. Until next time, keep your eyes on the horizon—and your broomsticks at the ready!

How Amazon Q reduced the time Amazon developers spent waiting for technical answers by 450k hours this year

Post Syndicated from Hank Cycyota original https://aws.amazon.com/blogs/devops/reducing-time-spent-waiting-with-amazon-q/

Introduction

Software development is complex and time consuming. Developers frequently need to stop building to get answers to hard, technical questions. What is the error in my code? How do I debug the logic? Where do I go to find this information? In 2024 Stack Overflow Developer Survey 53% of respondents agreed that waiting on answers disrupts their workflow, even if they know where to go find those answers. Similarly, 30% of respondents said knowledge silos impact their productivity ten times or more per week.

Our team – Amazon Software Builder Experience (ASBX) – leaned in to help solve this problem on behalf of Amazon developers. The ASBX organization has a mission to improve the software builder experience across tens of thousands of software engineers that work in all Amazon businesses. This includes the discovery tools that developers use to build and innovate on behalf of our customers. At Amazon, we have a wealth of software development expertise and an extensive knowledge base, but individual developers often have a hard time finding the exact information they need for the task at hand. They’re looking for a needle in a haystack. We have a few internal tools where Amazon developers go to connect with those subject matter experts (SME) but for the most complex questions, they might need to wait hours before they get a response. However, often the information they need is buried somewhere deep in our knowledge base. We saw an opportunity to pair new techniques in generative AI like retrieval augmented generation (RAG) with our extensive knowledge base to reduce the demand on those SMEs in the tools that our developers use every day. In this way, we would reduce the time our developers had to spend waiting for answers, allowing them stay in their workflow and continue delighting customers.

While we thought RAG would enable us to better assist our developers, we knew that our solution would need to pass rigorous security and privacy bars and scale to support the volume of documentation and questions that Amazon developers generate. Amazon Q Business met those requirements as well as removed some of the duplicative work that comes with managing a separate index and large language model. Additionally, Amazon Q Business comes with some out-of-the-box APIs that would allow us to integrate it with the tools that Amazon developers use as part of their discovery workflows. Finally, those APIs come with important hooks that would let us use the context within those tools to improve information retrieval and answer relevancy.

This year, Amazon Q Business has helped tens of thousands of Amazon developers answer questions and get back to building. With Amazon Q Business connected to our internal knowledge repositories, our developers are getting unblocked quicker to deliver results for customers; we’ve been able to reduce the time it takes for Amazon developers to find answers to technical questions from hours to just a few seconds. This year, Amazon Q Business has resolved over 1 million internal Amazon developer questions, reducing time spent churning on manual technical investigations by more than 450k hours.

Closing the discovery loop

Over its 30-year history, Amazon developers have generated a vast corpus of content to help them delight their customers. This includes community-generated content such as runbooks, dashboards, service-level documentation, structured Q&A, and program information. While we have an abundance of knowledge, finding the right answer to some of our more complex technical questions has been a challenge and the act of finding information pulls developers out of their workflow. When a developer is struggling to find an answer for a specific question, there are a few popular internal resources to get support from technical experts.

  • Developers can post a question on our internal Q&A boards (a tool called Sage) and wait for a reply. These questions are often complex in nature and require specific expertise to answer. The challenge is, answers aren’t immediate and the more obscure the question, the longer it can take for an SME to review and respond.
  • Developers can find the appropriate interest channel in Slack and ask there (often channels that are set up by a team of experts to provide support for a given problem domain). These questions are similarly complex and a developer benefits from asynchronous back and forth with the SME. This route can be faster than our Q&A boards, but it can still take hours to get a reply.

In both cases, the developer needs to wait for an answer from those SMEs to unblock their task. They could transition to the next task on their sprint board but now they need to context switch to something new (and often quickly switch back once the SME finally responds). What’s more, the answers to these questions often already exist somewhere in our knowledge base but the developer couldn’t find that needle in the collective haystack of tools and repositories that we have at Amazon.

We saw an opportunity to better connect Amazon developers with answers to their questions by integrating Amazon Q Business with those tools they were already using. We wanted this solution to take on the role of a subject matter expert in tools like Sage and Slack to provide a fast answer to questions to get the developer unstuck. We ingested our internal knowledge repository consisting of millions of documents into Amazon Q Business so our developers could get answers based on data across those repositories. Then, we integrated our Amazon Q Business instance with the tools where our developers commonly ask questions. Finally, we used context inherent to the tools themselves (e.g., the Slack channel in which a user was asking a question or the specific Sage subject they were posting to) to provide more useful responses. This approach has resulted in three primary benefits:

  • Swift adoption: We integrated the Q&A capabilities of Amazon Q Business into tools like Slack and Sage to make it part of the workflows our developers use every day. It also prevented us from having to create (yet) another tool that our developers needed to visit to get answers to questions. As a result, Amazon Q Business has already answered over 1 million internal Amazon developer questions this year.
  • More precise answers: This approach has enabled better responses to developer questions via Amazon Q Business by using the context that is readily available on the tool. In this way, we can narrow the retrieval scope from potentially millions of documents. Developers report that answers are more helpful when we scope retrieval in this manner (versus when no scope is present).
  • Faster answers: Leveraging Amazon Q Business has reduced the time it takes for a developer to get an answer to seconds, getting them unblocked so they can delight customers.

The remainder of this article touches briefly on how we set up our Amazon Q Business instance and how we integrated it with downstream tools. We then go into more depth around how we leveraged tool-specific context to provide better answers in service of getting our developers unstuck faster.

Setting up Amazon Q Business: Integrating Q Business with our knowledge repositories

We took a straightforward approach to setting up our Amazon Q Business application and followed the steps outlined in the service documentation here. We staged our knowledge repositories in S3 and used the Amazon Q Business S3 Connector to ingest those documents into our index (more info on how to use those S3 connectors here). We have a lot of content that isn’t useful in retrieval – pre-processing of our documents in S3 to remove stale content allows us to reduce our repository of over ten million potentially useful documents to under four million relevant ones. We also stage in S3 to enrich content with metadata (e.g., document type, team or service name, URL hierarchy) that isn’t in the source repository in order to take advantage of more Q features (e.g., filtering, boosting).

Integrating Amazon Q Business: Putting Q Business where users are

We leveraged a single Amazon Q Business application that hosts all our curated content to quickly release our desired experience in places where our developers ask questions. Also, leveraging one Amazon Q Business application lets us provide a consistent experience on the tools we integrate with, ensuring users are getting the same answers from the same dataset.

As we considered the UX for these integrations, our goal was that these experiences complement the existing workflow in the tools developers use. For instance, rather than increasing the cognitive load on the developer by adding a chat experience in the corner of Sage, we chose to automatically respond with answers to questions on the Q&A board. Similarly, developers can invoke Amazon Q Business on Slack in an interest channel to get answers to questions. We learned early that users have expectations around what sort of questions a bot is able to answer based on where they are interacting with it. For instance, customers were frustrated at a prototype experience on Sage that didn’t actually include the Q&A data native to that tool. Our prerequisite for onboarding new tools is to make sure we have the data for that tool and have considered other expectations users might have.

Enhancing our solution: Improving retrieval through surface-level context

By integrating Amazon Q Business directly with applications that developers use every day we’ve been able to leverage that information as context to improve retrieval accuracy and give better responses to developers. A common technical question our Amazon Q Business application answers is “How do I onboard to XX service?” Depending on the service in question (e.g., How common is the service name? Have many other actors onboarded to it?), Amazon Q Business might retrieve context outside of the authoritative set of documentation we want it to use. This can be confusing to the reader and reduce trust in the answers. However, if we know that a developer is asking that question in the XX-service@ Slack channel, we can narrow the retrieval scope to just the content related to XX-service.

As an example of where this helps, there is an internal Amazon service framework called Coral. Take a common question like “How do I maintain my Coral dashboard?” The answer to this question can vary by team, most of which maintain specific documentation about their dashboards. Asking this question without scoping will list a lot of good best practices around dashboards. However, if we know that the question is being asked in a specific team channel on Slack, it can use that context to scope to that specific team’s documentation to provide a more precise answer.

We scope documents with the Amazon Q Business filter attribute, which allows us to customize and control chat responses based on the document metadata reflected in index fields. We can break this down to the following steps:

  • We capture and associate document-specific metadata during ingestion.
  • We enable SMEs to define a topic space based on that metadata. A topic space is a collection of authoritative documents (spread across multiple repositories) specific to their knowledge domain. To use an example for an external-facing repository, a topic space could encompass all the Dynamo content on AWS Docs as specified by a root URL (like https://docs.aws.amazon.com/dynamodb). In this case, we would include all the URLs under that root in that topic space.
  • With the topic space defined, we leverage the Amazon Q Business metadata filters to restrict its RAG down at runtime to just the content in the topic space. Then, when a user asks a question about that topic space, say in a Slack interest channel on DynamoDB, the filters are applied and a domain-specific answer is generated from the SME-specified, authoritative content. Our call looks like this and the service documentation to set up your own is here.

  • In cases where the topic space does not contain an answer to the developer’s question (maybe the documentation hasn’t been updated or the question is too specific), we also added an opt-in user configuration to enable falling back to general knowledge. This ignores the filters and retries the original query to take advantage of the whole dataset.

Conclusion

This post described our process to integrating Amazon Q Business into tools where internal Amazon developers are looking for support. It also describes how we used context from these tools to improve the responses we provide to customers by using the inbuilt Amazon Q Business filter attributes.

You can also leverage this approach to make it easier for your developers to find answers to questions. For more information on how to do this, along with other ways you can make Amazon Q work for you, visit Getting started with Amazon Q Business.

Alexandru Baluta

Alexandru Baluta is a Senior Software Development Engineer at Amazon Web Services leading teams that specialize in Generative AI and Knowledge Discovery. His work enables enterprise decision-making, information flow, and knowledge sharing within Amazon’s engineering community and beyond. In his free time, he enjoys exploring new places and discovering unique restaurants.

Hank Cycyota

Hank Cycyota is Product Manager at Amazon Web Services and leads the teams that help Amazonians find the information they need so they can delight customers. He loves helping his teams work backwards from ambiguous problem statements to data-driven solutions. Outside of work, Hank spends time running and trying new recipes from his library of cookbooks.