VectorChat

An AI-powered data chat platform that allows users to upload company data files and have intelligent conversations with an AI assistant that can answer questions based on that data.

Features

• AI Chat Interface: Real-time conversations with an AI assistant
• Data Management: Upload and process company data files (.txt)
• Vector Database: Semantic search using Pinecone
• Knowledge Base: Intelligent responses based on uploaded documents
• Chat History: Persistent conversation management
• Authentication: Secure admin access
• Responsive Design: Works on mobile and desktop

Tech Stack

• Frontend: Next.js 15 with App Router
• Language: TypeScript
• Styling: Tailwind CSS v4.1
• State Management: Zustand
• AI/ML: LangChain, HuggingFace models
• Vector Database: Pinecone
• Authentication: Firebase Auth
• Database: Firebase Firestore
• Deployment: Vercel

Getting Started

Prerequisites

• Node.js 18+
• npm, yarn, pnpm, or bun

Installation

1. Clone the repository:

git clone https://github.com/DanyilLiubchakUk/VectorChat.git
cd vector-chat

2. Install dependencies:

npm install

3. Set up environment variables:

cp .env.example .env.local

4. Run the development server:

npm run dev

Open http://localhost:3000 with your browser to see the result.

Project Structure

src/
├── app/
│   ├── (auth)/          # Authentication pages
│   ├── (dashboard)/     # Dashboard and chat pages
│   ├── api/            # API routes
│   └── globals.css     # Global styles
├── components/         # Reusable components
├── lib/               # Utilities and configurations
├── store/             # State management
├── types/             # TypeScript type definitions
└── styles/            # Component styles

Environment Variables

Create a .env.local file with the following variables:

# Firebase
NEXT_PUBLIC_FIREBASE_API_KEY=
FIREBASE_PROJECT_ID=
FIREBASE_PRIVATE_KEY=
FIREBASE_CLIENT_EMAIL=

# Pinecone
PINECONE_API_KEY=
PINECONE_INDEX_NAME=

# AI/ML
HUGGINGFACE_API_KEY=
HUGGINGFACE_MODEL_NAME=

# App Configuration
NEXT_PUBLIC_APP_NAME=VectorChat
NEXT_PUBLIC_APP_DESCRIPTION=AI-powered data chat platform

Development Workflow

This project follows a strict pull request workflow:

Branch Strategy

• main - Production-ready code
• develop - Integration branch for features
• feature/* - New features (e.g., feature/chat-interface)
• bugfix/* - Bug fixes (e.g., bugfix/auth-error)
• hotfix/* - Critical production fixes

Pull Request Process

1. Create a feature branch from develop
2. Implement your changes with proper commits
3. Write tests for new functionality
4. Update documentation if needed
5. Create a pull request with detailed description
6. Code review and approval required
7. Merge to develop after approval
8. Deploy to staging for testing
9. Merge develop to main for production

Commit Convention

feat: add user authentication
fix: resolve chat history loading issue
docs: update API documentation
style: improve button component styling
refactor: optimize vector search algorithm
test: add unit tests for data processing
chore: update dependencies

Deployment

This project is optimized for deployment on Vercel:

1. Push your code to GitHub
2. Connect your repository to Vercel
3. Add your environment variables in Vercel dashboard
4. Deploy!