RAG-Stocks: AI-Powered Stock Research and Recommendation Platform

Overview

RAG-Stocks is an advanced stock research and recommendation platform that leverages cutting-edge AI technologies to provide intelligent insights into financial markets. By combining Retrieval-Augmented Generation (RAG), real-time trading data, and natural language processing, this application offers comprehensive stock analysis and personalized investment recommendations.

Features

• AI-Powered Stock Analysis
• Real-time Market Data
• Natural Language Query Interface
• Intelligent Stock Recommendations
• Portfolio Tracking

Tech Stack

• Frontend: Next.js (React)
• Backend: Python
• AI Technologies:
  • LangChain
  • Weaviate Vector Database
• Trading API: Alpaca
• Database: Neon PostgreSQL

Prerequisites

• Python 3.9+
• Node.js 18+
• Docker (optional)

Installation

Clone the Repository

git clone https://github.com/laukikk/RAG-Stocks.git
cd RAG-Stocks

Backend Setup

cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Frontend Setup

cd ../frontend
npm install

Environment Configuration (all FREE plans)

1. Copy .env.example to .env
2. Fill in required API keys:
   • Alpaca Trading
   • Neon Database
   • OpenAI
   • Weaviate
   • GitHub Token: optional (I'm using GitHub Models)

Running the Application

Development Mode

# Start Backend
cd backend
python src/main.py

# Start Frontend (in another terminal)
cd frontend
npm run dev

Docker Deployment (To-Do)

docker-compose up --build

Testing (To-Do)

# Run Backend Tests
cd backend
python -m pytest tests/

# Run Frontend Tests
cd frontend
npm test