Wine-LLM – AI-Powered Wine Recommendation System

An AI-powered Retrieval-Augmented Generation (RAG) application that provides personalized wine recommendations based on user prompts, utilizing multiple Large Language Models (LLMs) for comparison.

Overview

Wine-LLM is a web-based application designed to assist users in selecting the perfect wine for various occasions. By leveraging a Retrieval-Augmented Generation (RAG) approach, the system processes user inputs to provide tailored wine suggestions. Users can compare responses from different LLMs, including OpenAI, Llama, and a custom RAG model, to choose the recommendation that best suits their preferences.

Key Features

• Personalized Wine Recommendations: Input scenarios like "I'm having seafood tonight; what French wine pairs well?" to receive tailored suggestions.
• LLM Comparison: Evaluate and compare responses from OpenAI, Llama, and a custom RAG model.
• Interactive Web Interface: User-friendly frontend built with Next.js for seamless interactions.
• Backend Processing: Flask-based backend handles API requests, model interactions, and data retrieval.
• Embedding Generation: Utilize the Wine_RAG.ipynb notebook to process and store embeddings for efficient data retrieval.

Tech Stack

• Frontend: Next.js (React)
• Backend: Flask (Python)
• LLMs: OpenAI API, Llama, Custom RAG Model
• Environment Management: Python's venv and requirements.txt

🛠️ Installation & Setup

Prerequisites

• Python 3.7+
• Node.js and npm
• OpenAI API Key: Obtain from OpenAI
• Groq API Key: Obtain from Groq

Clone the Repository

git clone https://github.com/MozartofCode/Wine-LLM.git
cd Wine-LLM

Backend Setup

1. Create a Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install Dependencies:

   pip install -r requirements.txt

3. Set Environment Variables:

   Create a .env file in the root directory and add:

   OPENAI_API_KEY=your_openai_api_key
   GROQ_API_KEY=your_groq_api_key

4. Generate Embeddings:

   Run the Wine_RAG.ipynb notebook to process wine data and generate embeddings. Ensure the resulting embeddings are saved appropriately for the backend to access.

5. Start the Flask Server:

   python app.py

   The backend will run on http://localhost:5000.

Frontend Setup

1. Navigate to the Frontend Directory:

   cd frontend

2. Install Dependencies:

   npm install

3. Start the Frontend Server:

   npm run dev

   The application will be accessible at http://localhost:3000.

🎯 Usage

1. Access the Application:

   Open your browser and navigate to http://localhost:3000.

2. Input Your Scenario:

   Enter a description of your meal or occasion, such as:

   • "I'm having grilled salmon tonight; what wine would pair well?"
   • "Looking for a wine to accompany dark chocolate dessert."

3. Select LLM for Recommendation:

   Choose between OpenAI, Llama, or the custom RAG model to generate a wine recommendation.

4. View and Compare Results:

   Analyze the suggestions provided by each model to select the most suitable wine.

Future Enhancements

• Enhanced Database Integration: Incorporate a comprehensive wine database for more accurate recommendations.
• Feedback Mechanism: Enable users to provide feedback on recommendations to improve model accuracy.

Contact

Author: Bertan Berker
📧 Email: bb6363@rit.edu
💻 GitHub: MozartofCode

Author: Jacob Sakelarios
📧 Email: 💻 GitHub: