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    Create a generative AI-based application builder assistant using Amazon Bedrock Agents

    In this post, we set up an agent using Amazon Bedrock Agents to act as a software application builder assistant.

    Agentic workflows are a fresh new perspective in building dynamic and complex business use- case based workflows with the help of large language models (LLM) as their reasoning engine or brain. These agentic workflows decompose the natural language query-based tasks into multiple actionable steps with iterative feedback loops and self-reflection to produce the final result using tools and APIs.

    Amazon Bedrock Agents helps you accelerate generative AI application development by orchestrating multistep tasks. Amazon Bedrock Agents uses the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps. They use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide a final response to the end user. This offers tremendous use case flexibility, enables dynamic workflows, and reduces development cost. Amazon Bedrock Agents is instrumental in customization and tailoring apps to help meet specific project requirements while protecting private data and securing their applications. These agents work with AWS managed infrastructure capabilities and Amazon Bedrock, reducing infrastructure management overhead. Additionally, agents streamline workflows and automate repetitive tasks. With the power of AI automation, you can boost productivity and reduce cost.

    Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

    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.

    The agent can generate SQL queries using natural language questions using a database schema DDL (data definition language for SQL) and execute them against a database instance for the database tier.

    We use Amazon Bedrock Agents with two knowledge bases for this assistant. Amazon Bedrock Knowledge Bases inherently uses the Retrieval Augmented Generation (RAG) technique. A typical RAG implementation consists of two parts:

    A data pipeline that ingests data from documents typically stored in Amazon Simple Storage Service (Amazon S3) into a knowledge base, namely a vector database such as Amazon OpenSearch Serverless, so that it’s available for lookup when a question is received
    An application that receives a question from the user, looks up the knowledge base for relevant pieces of information (context), creates a prompt that includes the question and the context, and provides it to an LLM for generating a response

    The following diagram illustrates how our application builder assistant acts as a coding assistant, recommends AWS design best practices, and aids in SQL code generation.

    Based on the three workflows in the preceding figure, let’s explore the type of task you need for different use cases:

    Use case 1 – If you want to write and validate a SQL query against a database, use the existing DDL schemas set up as knowledge base 1 to come up with the SQL query. The following are sample user queries:

    What are the total sales amounts by year?
    What are the top five most expensive products?
    What is the total revenue for each employee?

    Use case 2 – If you want recommendations on design best practices, look up the AWS Well-Architected Framework knowledge base (knowledge base 2). The following are sample user queries:

    How can I design secure VPCs?
    What are some S3 best practices?

    Use case 3 – You might want to author some code, such as helper functions like validate email, or use existing code. In this case, use prompt engineering techniques to call the default agent LLM and generate the email validation code. The following are sample user queries:

    Write a Python function to validate email address syntax.
    Explain the following code in lucid, natural language to me. $code_to_explain (this variable is populated using code contents from any code file of your choice. More details can be found in the notebook).

    Prerequisites

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

    Clone the GitHub repository and follow the steps explained in the README.
    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
    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:

    Choose the underlying FM for your agent.
    Write a clear and concise agent instruction to use one of the two knowledge bases and base agent LLM. (Examples given later in the post.)
    Create and associate an action group with an API schema and a Lambda function.
    Create, associate, and ingest data into the two knowledge bases.
    Create, invoke, test, and deploy the agent.
    Generate UI and backend code with LLMs.
    Recommend AWS best practices for system design with the AWS Well-Architected Framework guidelines.
    Generate, run, and validate the SQL from natural language understanding using LLMs, few-shot examples, and a database schema as a knowledge base.
    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</p><p>Feel free to ask any questions
    along those lines!

    Each user question to the agent by default includes the following system prompt.

    Note: The following system prompt remains the same for each agent invocation, only the {user_question_to_agent} gets replaced with user query.

    Question: {user_question_to_agent}

    Given an input question, you will use the existing Knowledge Bases on AWS
    Well-Architected Framework and Northwind DB Knowledge Base.

    – For building and designing software applications, you will use the existing Knowledge Base on AWS well-architected framework
    to generate a response of the most relevant design principles and links to any documents. This Knowledge Base response can then be passed
    to the functions available to answer the user question. The final response to the direct answer to the user question.
    It has to be in markdown format highlighting any text of interest. Remove any backticks in the final response.

    – To generate code for a given user question,  you can use the default Large Language model to come up with the response.
    This response can be in code markdown format. You can optionally provide an explanation for the code.

    – To explain code for a given user question, you can use the default Large Language model to come up with the response.

    – For SQL query generation you will ONLY use the existing database schemas in the Northwind DB Knowledge Base to create a syntactically
    correct SQLite query and then you will EXECUTE the SQL Query using the functions and API provided to answer the question.

    Make sure to use ONLY existing columns and tables based on the Northwind DB database schema. Make sure to wrap table names with
    square brackets. Do not use underscore for table names unless that is part of the database schema. Make sure to add a semicolon after
    the end of the SQL statement generated.</p><p>Remove any backticks and any html tags like <table><th><tr> in the
    final response.

    Here are a few examples of questions I can help answer by generating and then executing a SQLite query:

    – What are the total sales amounts by year?</p>
    – What are the top 5 most expensive products?</p>
    – What is the total revenue for each employee?</p>

    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.

    The order of resource cleanup is as follows:

    Disable the action group.
    Delete the action group.
    Delete the alias.
    Delete the agent.
    Delete the Lambda function.
    Empty the S3 bucket.
    Delete the S3 bucket.
    Delete IAM roles and policies.
    Delete the vector DB collection policies.
    Delete the knowledge bases.

    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.

    Beyond code generation and explanation of code as demonstrated in this post, to run and troubleshoot application code in a secure test environment, you can refer to Code Interpreter setup with Amazon Bedrock Agents

    For more information on creating agents to orchestrate workflows, see Amazon Bedrock Agents.

    Acknowledgements

    The author thanks all the reviewers for their valuable feedback.

    About the Author

    Shayan Ray is an Applied Scientist at Amazon Web Services. His area of research is all things natural language (like NLP, NLU, NLG). His work has been focused on conversational AI, task-oriented dialogue systems and LLM-based agents. His research publications are on natural language processing, personalization, and reinforcement learning.

    Source: Read More 

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