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This project is a fullstack AI chat application that integrates a backend powered by Spring AI with the Ollama model and a frontend developed using the Hilla framework. Both the frontend and backend are hosted on the same server, providing a unified and seamless user experience.

CSS 20.74% TypeScript 54.98% Java 24.28%
ai hilla ollama reactive spring spring-ai

llm-spring-hilla's Introduction

Fullstack AI Chat Application

Overview

This project is a fullstack AI chat application that integrates a backend powered by Spring AI with the Ollama model and a frontend developed using the Hilla framework. Both the frontend and backend are hosted on the same server, providing a unified and seamless user experience.

Features

  • AI-Powered Chat: Uses the Ollama model via Spring AI to deliver intelligent and context-aware chat responses.
  • Integrated Frontend and Backend: Built with the Hilla framework, ensuring a cohesive application where both components operate on the same server.
  • Responsive Interface: Offers an interactive and modern chat experience within the web application.

Technologies

  • Java 17: The application is developed using Java 17.
  • Spring AI: Framework for integrating AI models into Java applications.
  • Ollama Model: Advanced AI model for generating chat responses.
  • Hilla Framework: Combines Vaadin and Spring Boot for a unified fullstack solution.
  • Docker Compose: Used for managing and running the Ollama container.

Getting Started

Prerequisites

  • JDK 17
  • Maven
  • Docker and Docker Compose

Installation

  1. Clone the Repository:

    git clone https://github.com/abbes-larbaoui/llm-spring-hilla.git
    cd llm-spring-hilla
  2. Run the Docker Compose File:

    • Navigate to the directory containing the docker-compose.yml file.
    • Start the Ollama container:
     docker-compose up -d
  3. Run the Llama2 Model:

    • Start the Llama2 model within the Ollama container using::
     docker exec -it ollama ollama run llama2
  4. Build and Run the Application:

    • Navigate to the project root directory.
    • Install dependencies and build the application with Maven:
     mvn clean install
    • Run the Spring Boot application, which will start both the backend and frontend:
     mvn spring-boot:run
  5. Access the Application:

    • Open your web browser and go to http://localhost:8080 to interact with the chat application.

Usage

  • Chat Interface: Use the integrated chat interface to interact with the AI model.
  • Configuration: Modify AI model parameters and other settings in the src/main/resources/application.yml file.

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