Multi-Turn AI Chatbot with LLaMA 3 [AI / NLP / Conversational AI]

Project Overview

This project is a Multi-Turn AI Chatbot System developed using LLaMA 3 as the core conversational AI engine. The chatbot supports multi-turn conversations, user authentication, feedback collection, analytics, and local AI inference.

Phase 1 Objectives

Phase 1 focuses on building the complete system foundation including:

• Frontend Chat Interface
• Backend API Server
• Google OAuth Authentication
• LLaMA 3 Integration
• Database Connectivity
• Feedback Collection System
• Analytics Module Initialization

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Tech Stack

I'm facing difficulty running LLaMA 3 locally due to limited hardware resources So I'm using grok or openRouter api

This project uses an upgraded modern stack while maintaining all required functionality.

Technology	Purpose
Python	Backend development
FastAPI / Flask	Backend APIs
Next.js	Frontend framework
Ollama	Local LLaMA serving
Supabase	Database

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Project Architecture

Main Components

1. Frontend Web Application

Built using Next.js.

Responsible for:

• Login/signup pages
• Google authentication
• Chat dashboard
• Chat interface
• Feedback collection

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2. Backend API Server

Responsible for:

• API routing
• Session management
• Authentication
• AI request handling
• Database communication

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3. LLaMA 3 Inference Engine

Responsible for:

• Generating chatbot responses
• Processing prompts locally
• Maintaining conversational context

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4. Database

Built using supabase.

Stores:

• User accounts
• Sessions
• Chat history
• Feedback data
• Analytics data

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5. Analytics Module

Responsible for:

• Feedback analysis
• Usage analytics
• Topic classification
• Graph generation

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Requirements

• docker
• supabase cli
• Python > 3.8
• Next.js 16.2.6 stable version
• uv (Python package manager)

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How to Run This Project

Follow these steps to set up and run the project locally:

1. Clone the Repository

git clone <repository-url>
cd MultiturnAiChatbotWithLlama3

2. Start Supabase (Requires Docker)

Ensure Docker is running on your system, navigate to the frontend directory, and start the Supabase local environment:

cd frontend
supabase start

Note: This will provide you with several URLs and keys. You will need these for the next steps.

3. Configure and Run Frontend

Copy the example environment file and update it with your local Supabase credentials:

# Assuming you are still in the frontend directory
cp .env.example .env.local

Edit .env.local and fill in the values provided by the supabase start command:

• NEXT_PUBLIC_SUPABASE_URL (usually http://127.0.0.1:54321)
• NEXT_PUBLIC_SUPABASE_ANON_KEY
• SUPABASE_SERVICE_ROLE_KEY
• GROQ_API_KEY (if using Groq)
• OPENROUTER_API_KEY (if using OpenRouter)

Install dependencies and run:

npm install
npm run dev

The frontend will be available at http://localhost:3000.

4. Configure and Run Backend

Open a new terminal window:

cd MultiturnAiChatbotWithLlama3/backend
cp ../frontend/.env.local .env

Install dependencies using uv and start the server:

uv pip install -r requirements.txt # Or run directly:
uv run python app.py

The backend API will be available at http://localhost:5000.