Knowledge Factory

Actors and Components User : Interacts with the application through the Web Interface (frontend). Frontend : Web Interface : Displays a user-friendly interface. Collects input (prompt) from the user. Sends the input to the backend for processing. Backend : Flask Application : Processes user input and communicates with Amazon Bedrock API. Handles requests and returns AI-generated responses to the frontend. Amazon Bedrock : Foundation Models : Provides the AI capabilities (e.g., text generation, summarization). Responds to API requests made by the Flask application. Deployment : Two deployment options: AWS Elastic Beanstalk : A managed service for deploying and scaling web applications. AWS EC2 : A virtual server in the cloud for custom deployment and management. Workflow User Interaction : The user inputs a prompt via the Web Interface. Request Handling : The Web Interface sends the prompt to the Flask backend. AI Processing : The Flask application forwards the request to the Amazon Be...

Explanation: Spring Boot : Represents the Spring Boot application. Testcontainers : This section represents the Testcontainers framework that spins up the Vault container for integration testing. Vault Container : Represents the Vault container that is managed by Testcontainers. Vault : Represents HashiCorp Vault itself. To integrate Spring Boot with HashiCorp Vault using Testcontainers for end-to-end testing, we need to follow these steps: Docker Setup   Install Docker:  Download and install Docker from Docker's official website . Windows/macOS: Install and run Docker Desktop. Linux: Install Docker Engine using your package manager. Verify Installation: Run docker --version to confirm Docker is installed. Start Docker: Windows/macOS: Launch Docker Desktop and ensure it’s running. Linux: Start Docker with sudo systemctl start docker . Add Dependencies Add the required dependencies in your pom.xml for Spring Boot, HashiCorp Vault, and Testcontainers. < properties > ...

The integration of AI capabilities into modern applications has become a critical trend in software development. With frameworks like Spring Boot and AWS Bedrock, developers can efficiently build robust, scalable, and AI-driven applications. This guide provides a step-by-step process to create a Spring Boot application integrated with Bedrock AI using the spring-ai-bedrock-ai-spring-boot-starter library. Technologies Involved 1. Spring Boot Spring Boot is a popular framework for building Java-based microservices and applications. It simplifies application setup with its opinionated approach and provides essential features such as dependency management, embedded servers, and auto-configuration. Advantages: Rapid development with minimal configuration. Rich ecosystem of libraries and extensions. Production-ready tools like Actuator for monitoring and metrics. 2. AWS Bedrock AWS Bedrock provides developers with the ability to build and scale generative AI applications using pre-trained f...

Here's a brief explanation of the diagram: User logs in: The user sends a POST request to the /login endpoint. API processes request: The API forwards this request to the Auth Controller for login processing. Check credentials: The Auth Controller checks the user credentials in the mock database. Generate JWT: If credentials are valid, the Auth Controller calls Token Utils to generate a JWT. Return JWT: The generated JWT is returned to the API and then back to the user. Access protected resource: The user sends a GET request to the /auth/protected endpoint with the JWT in the Authorization header. Validate JWT: The API uses JWT Middleware to validate the JWT by calling Token Utils . Claims validation: Token Utils returns the claims to JWT Middleware , which allows the request to proceed. Resource access: The API processes the request and returns the protected resource to the user. This guide provides a comprehensive walkthrough for implementing JSON Web Token (JWT)...

This diagram illustrates the Command Query Responsibility Segregation (CQRS) design pattern applied to a microservices architecture. Here's a breakdown of its components: Key Elements: Microservices : Order Microservice : Handles commands (e.g., create, update, delete orders) through the Command Service and Command Model . Handles queries (e.g., fetching order details) via the Query Service and Query Model . Customer Microservice : Handles customer-related commands through its Command Service and Command Model . Handles customer queries via its Query Service and Query Model . Endpoints : Command Endpoint : Processes write operations. Query Endpoint : Processes read operations. Event Store : Centralized storage for events generated by the command operations. Ensures consistency and event sourcing. Read Storage : Optimized for query operations, providing fast access to read-only data. Message Broker : Facilitates communication between microservices by publishing and consuming ev...

Here’s a guide to generate a Docker image for a Spring Boot application using three different methods . Each method has its use case, depending on your project's needs and complexity. 1. Using a Dockerfile This method gives you full control over the image creation process. Steps: Create a Dockerfile : Create a file named Dockerfile in the root directory of your Spring Boot project with the following content: FROM openjdk: 17 -jdk-slim ARG JAR_FILE=target/app.jar COPY ${JAR_FILE} app.jar ENTRYPOINT [ "java" , "-jar" , "/app.jar" ] Build the JAR file: Package your application into a JAR file: mvn clean package Build the Docker image: Run the following command to build the Docker image: docker build -t spring- boot - app . Run the Docker container: Start the container: docker run -p 8080:8080 spring- boot - app 2. Using Jib (Maven/Gradle Plugin) Jib is a tool from Google that simplifies the process of creating Docker images without needing a D...