Create a Generative AI-Based Application Builder

Solution Overview

Typically, a three-tier software application has a UI interface tier, a middle tier (the backend) for business APIs, and a database tier. The generative AI-based application builder assistant from this post will help you accomplish tasks through all three tiers. It can generate and explain code snippets for UI and backend tiers in the language of your choice to improve developer productivity and facilitate rapid development of use cases. The agent can recommend software and architecture design best practices using the AWS Well-Architected Framework for the overall system design.

Prerequisites

To run this solution in your AWS account, complete the following prerequisites:

1. Clone the GitHub repository and follow the steps explained in the README.
2. Set up an Amazon SageMaker notebook on an ml.t3.medium Amazon Elastic Compute Cloud (Amazon EC2) instance. For this post, we have provided an AWS CloudFormation template, available in the GitHub repository. The CloudFormation template also provides the required AWS Identity and Access Management (IAM) access to set up the vector database, SageMaker resources, and AWS Lambda.
3. Acquire access to models hosted on Amazon Bedrock. Choose Manage model access in the navigation pane on the Amazon Bedrock console and choose from the list of available options. We use Anthropic’s Claude v3 (Sonnet) on Amazon Bedrock and Amazon Titan Embeddings Text v2 on Amazon Bedrock for this post.

Implement the Solution

In the GitHub repository notebook, we cover the following learning objectives:

1. Choose the underlying FM for your agent.
2. Write a clear and concise agent instruction to use one of the two knowledge bases and base agent LLM.
3. Create and associate an action group with an API schema and a Lambda function.
4. Create, associate, and ingest data into the two knowledge bases.
5. Create, invoke, test, and deploy the agent.
6. Generate UI and backend code with LLMs.
7. Recommend AWS best practices for system design with the AWS Well-Architected Framework guidelines.
8. Generate, run, and validate the SQL from natural language understanding using LLMs, few-shot examples, and a database schema as a knowledge base.
9. Clean up agent resources and their dependencies using a script.

Agent Instructions and User Prompts

The application builder assistant agent instruction looks like the following:

Hello, I am AI Application Builder Assistant. I am capable of answering the following three categories of questions:

• Best practices for design of software applications using the content inside the AWS best practices and AWS well-architected framework Knowledge Base. I help customers understand AWS best practices for building applications with AWS services.
• Generate a valid SQLite query for the customer using the database schema inside the Northwind DB knowledge base and then execute the query that answers the question based on the [Northwind] dataset. If the Northwind DB Knowledge Base search function result did not contain enough information to construct a full query try to construct a query to the best of your ability based on the Northwind database schema.
• Generate and Explain code for the customer following standard programming language syntax.
  Feel free to ask any questions along those lines!

Cost Considerations

The following are important cost considerations:

• This current implementation has no separate charges for building resources using Amazon Bedrock Knowledge Bases or Amazon Bedrock Agents.
• You will incur charges for embedding model and text model invocation on Amazon Bedrock. For more details, refer to Amazon Bedrock pricing.
• You will incur charges for Amazon S3 and vector DB usage. For more details, see Amazon S3 pricing and Amazon OpenSearch Service Pricing, respectively.

Clean Up

To avoid incurring unnecessary costs, the implementation automatically cleans up resources after an entire run of the notebook. You can check the notebook instructions in the Clean-up Resources section on how to avoid the automatic cleanup and experiment with different prompts.

Conclusion

This post demonstrated how to query and integrate workflows with Amazon Bedrock Agents using multiple knowledge bases to create a generative AI-based software application builder assistant that can author and explain code, generate SQL using DDL schemas, and recommend design suggestions using the AWS Well-Architected Framework.

FAQs

Q: Can I use this solution with other knowledge bases or LLMs?
A: Yes, you can use this solution with other knowledge bases or LLMs.

Q: How do I handle errors and exceptions in the agent?
A: You can handle errors and exceptions in the agent by adding try-catch blocks and logging mechanisms.

Q: Can I use this solution with other programming languages?
A: Yes, you can use this solution with other programming languages.

Q: How do I troubleshoot issues with the agent?
A: You can troubleshoot issues with the agent by checking the agent’s logs and error messages.

Q: Can I use this solution with other AWS services?
A: Yes, you can use this solution with other AWS services.