☕ NYRU Cafe Chatbot

A Retrieval-Augmented Generation (RAG) chatbot designed for NYRU COFFEE & JUICE, built using Streamlit, the Gemini API, and a TiDB Vector Database.

Features

• Interactive Chat Interface
• RAG Architecture
• Powered by Gemini
• TiDB Vector Search

Tech Stack

• Frontend / UI: Streamlit
• LLM: Google Gemini (google-generativeai)
• Embeddings: Sentence Transformers (BAAI/bge-m3)
• Database: TiDB (MySQL compatible with Vector search capabilities)
• Data Ingestion: Pandas

Local Setup

Prerequisites

1. Python 3.12
2. A Gemini API Key from Google AI Studio.
3. A TiDB Cloud Serverless Cluster configured with a documents table for storing vector embeddings.

Installation

1. Clone the repository (if applicable) and navigate to the project directory.

2. Create a virtual environment:

   python -m venv .venv

3. Activate the virtual environment:

   • Windows: .\.venv\Scripts\activate
   • Mac/Linux: source .venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

5. Configure Environment Variables: Create a .env file in the root directory based on your credentials:

   TIDB_HOST=your_tidb_host
   TIDB_PORT=4000
   TIDB_USER=your_tidb_user
   TIDB_PASSWORD=your_tidb_password
   TIDB_DATABASE=RAG
   TIDB_SSL_CA=isrgrootx1.pem
   GEMINI_API_KEY=your_gemini_api_key

Running the App

Start the Streamlit application by running:

streamlit run app.py

Deployment

This application is designed to be easily deployed to Streamlit Community Cloud. Simply connect your GitHub repository and configure your .env keys in the Streamlit Cloud Secrets Management dashboard.