StockRecommenderForBeginner

Project Overview

Investing can be intimidating for beginners due to the overwhelming amount of information—news, short videos, and endless data. StockRecommenderForBeginner is an AI-powered platform designed to simplify the investment process for beginners. It provides stock details, price trends, market news, and answers to finance-related questions. The platform also features a RAG-based local knowledge base, allowing users to export insights with one click and interact with it through Q&A. You can upload files in almost any format—text, images, videos, and more—into the knowledge base, where it automatically generates summaries. Our mission is to help beginners confidently kickstart their investment journey!

Tech Stack

The application is built with the following components:

• Backend: A Java-based Spring Boot Maven project.
• Knowledge Base: A Python-based RAG (Retrieval-Augmented Generation) system.
• Database: MySQL for storing data.
• Frontend: React and JSP

Prerequisites

To run the application, ensure you have the following installed:

• Java: Required for the Spring Boot backend.
• Maven: Used to build and manage the Java project.
• Python: Necessary for the RAG knowledge base.
• MySQL: Must be installed and running.
• API keys: In the application.properties file, include:
  • Your MySQL database password.
  • OpenRouter API key (for external AI models).
  • Polygon API key (for real-time stock data retrieval).

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(Optional) Docker: It is optional but recommended for containerizing the RAG knowledge base.

Install these tools on your system before proceeding. If you do not want to use the Knowledge Base, then docker is not needed.

AI Chat Functionality

The AI Chat feature provides flexible options for users:

• Local Models: Run any model locally using LM Studio. We recommend the LLaMA 3.2 1B model for beginners due to its ease of use and efficiency.
• External Models: Connect to the OpenRouter API to access powerful external large language models for enhanced performance.

This dual-mode setup lets you choose the best option based on your needs and available resources.

Using AI Chat

To chat only in local, we use LM Studio. Here’s how to set it up:

1. Download and install LM Studio from lmstudio.ai.
2. Load the LLaMA 3.2 1B model (recommended for beginners).
3. Start the model and run the server.

Docker Integration

Docker support is included to streamline the RAG knowledge base setup. To enable it, follow these steps:

Configure Docker:

• Edit the docker-compose.yml file and add your MySQL database password.
• Update the application-docker.properties file with matching database credentials.

Starting the Application

Follow these steps to launch the app:

Starting the java app only in local:

0. Start the MySQL Server:
   • Update the mysql password in the application.properties file.
   • Verify the database schema exists. If not, run the create_all_tables.sql script to set up the required tables.
1. Install Maven:
   • Ensure Maven is installed.
   • Run mvn clean install to build the project.
2. Start the Server:
   • Use mvn spring-boot:run to launch the application.
3. Test the Application:
   • Open a browser and go to localhost:8080 to confirm it’s running.

Database Check Tool

A handy tool is available at the bottom of the Login page to inspect your data. Alternatively, use a database workbench to view it directly.

Starting the fully functional app with Docker Compose

Docker Compose is a tool that allows you to define and run multi-container Docker applications with a single command. In this project, it simplifies the setup of the RAG knowledge base. Here’s how to start it:

Prerequisites

Before running the command, make sure you have the following ready:

• Docker: Installed on your system. If you don’t have it, download and install it from docker.com.
• docker-compose.yml: This file should be in your project directory and configured with your MySQL database password.
• application-docker.properties: Updated with the correct database credentials to match docker-compose.yml.

Command to Start Docker Compose

Open your terminal, navigate to the directory containing docker-compose.yml, and run:

docker-compose build

(This might take times...)

docker-compose up

• docker-compose up: This builds (if needed) and starts all the containers defined in the docker-compose.yml file.

What Happens Next?

Once you execute the command:

• Docker Compose will pull any required images (e.g., MySQL, RAG services) from Docker Hub if they’re not already on your system.
• It will then start the containers as specified in the configuration.
• Always check the containers in Docker Desktop to make sure they are running.

Stopping the Containers

When you’re done, you can stop and remove the containers with:

docker-compose down

This cleanly shuts down the services and removes the containers, though your data will persist if you’ve configured volumes in docker-compose.yml. You can also use system prune to clear the cache and volume if needed.

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That’s it! With the docker-compose up command, you’ll have your project’s containers up and running in no time. Let me know if you need help troubleshooting!