Demo Microservices

Table of Contents

1. Introduction
2. Architecture
3. Microservices
   • API Gateway
   • Eureka Server
   • Config Server
   • Faculty Microservice
   • Report Microservice
   • OpenAI Microservice
4. Prerequisites
5. Running the application
6. Application links

Introduction

This demo application showcases the implementation of Spring Boot microservices, highlighting their powerful features and capabilities, with a particular emphasis on integrating OpenAI for advanced AI functionalities.

Architecture

• Java 21: The latest long-term support (LTS) version of Java, providing modern language features and performance improvements.
• Spring Framework 6: A comprehensive framework for enterprise applications, providing support for dependency injection, aspect-oriented programming, and more.
• Spring Boot 3: A convention-over-configuration solution to create stand-alone, production-grade Spring-based applications with ease.
• PostgreSQL: An advanced open-source relational database with robust features and performance.
• pgvector: An extension for PostgreSQL that provides vector similarity search, enhancing capabilities for machine learning applications.
• Liquibase: A database schema change management solution that allows for tracking, versioning, and deploying database changes.
• Apache Kafka: A distributed streaming platform used for building real-time data pipelines and streaming applications.
• OpenAPI (formerly known as Swagger): A specification for defining and documenting RESTful APIs. The OpenAPI Specification (OAS) enables interactive documentation, code generation, and API client creation.
• Jasper Reports: An open-source reporting tool for creating, displaying, and exporting reports in various formats.
• OpenAI: Integrates with OpenAI's GPT models to provide advanced natural language processing capabilities.
• RAG (Retrieval-Augmented Generation) with OpenAI: Combines retrieval of relevant documents and generation of responses using OpenAI's models to enhance the accuracy and relevance of generated content.
• Docker: A platform that enables the creation, deployment, and management of containerized applications, ensuring consistency across multiple environments and simplifying deployment processes.

Microservices

API Gateway

The API Gateway acts as the single entry point for all client requests. It routes incoming requests to the appropriate service, ensuring secure and efficient communication between clients and backend services.

Eureka Server

The Eureka Server is used for service discovery. It maintains a registry of all the microservices, allowing them to discover each other and enabling dynamic scaling and fault tolerance within the system.

Config Server

The Config Server provides centralized configuration management. It allows all microservices to retrieve their configurations from a central place, ensuring consistency and simplifying the management of configuration changes.

Faculty Microservice

The Faculty Microservice uses Liquibase scripts to create tables in database, insert test data and it is responsible for handling all operations related to faculty members. This includes CRUD operations, data processing, and interactions with other services as needed. It also operates as a Kafka consumer, continuously listening for and processing messages from specified Kafka topic to handle real-time data streams efficiently.

Report Microservice

The Report Microservice calls the OpenAI microservice synchronously via Feign (Declarative REST Client) to generate a summary for a course. It then creates a PDF report using Jasper Reports. Additionally, it acts as a Kafka producer, sending the course summary to a specified Kafka topic.

OpenAI Microservice

The OpenAI Microservice integrates with OpenAI's API to provide advanced AI functionalities. OpenAI is an artificial intelligence research organization that provides powerful models like GPT (Generative Pre-trained Transformer) for various AI applications including natural language processing, text generation, and more.

Key features of the OpenAI Microservice include:

1. Summary Generation: Uses the Spring AI chat client to obtain summaries from OpenAI based on provided content.

2. Data Synchronization: Syncs student data from a relational database to a vector database using Spring AI Vector Store. This facilitates efficient storage and retrieval of high-dimensional data.

3. RAG (Retrieval-Augmented Generation): Performs similarity searches for a given question over the local vector database to find relevant data. The relevant data, which is based on our own datasets that OpenAI is not trained on, is then sent to OpenAI to generate a contextually appropriate response. This feature enhances the accuracy and relevance of generated responses by leveraging local, proprietary data.

These functionalities enable the OpenAI Microservice to support a wide range of AI-driven applications, enhancing the overall capabilities of the system.

Prerequisites

• Java 21 or higher
• Maven
• Docker

Running the Application

1. Clone the repository:

   git clone https://github.com/vukmanovicmilos/web-app.git

2. Run maven:

   mvn install

3. To run the application, navigate to the root directory of the project and execute the following command:

   docker compose up
   

*Don't forget to set your API key in the docker-compose.yml file:

SPRING_AI_OPENAI_API_KEY = <YOUR_API_KEY>

Application links

Some important links

• Eureka: http://localhost:8761/
• Config Server (yml for faculty): http://localhost:8888/faculty/default
• Faculty Open API (Swagger) documentation: http://localhost:9100/swagger-ui/index.html
• Generate PDF report via API Gateway: http://localhost:8765/report/reports/3
• Sync students from relational to vector database via API Gateway: http://localhost:8765/open-ai/open-ai/sync-students
• Get response from OpenAI for question (default) via API Gateway: http://localhost:8765/open-ai/open-ai/question