Ask Racha

A web application that provides an AI-powered interface to interact with Storacha's documentation. The application uses advanced language models and RAG (Retrieval-Augmented Generation) to provide accurate and contextual responses.

Features

• Interactive chat interface
• Integration with Google's Gemini AI model
• Web-based documentation retrieval
• Real-time response generation
• Modern UI built with Next.js and Tailwind CSS

Tech Stack

Frontend

• Next.js 15.3.3
• React 19
• TypeScript
• Tailwind CSS
• Lucide React (for icons)

Backend

• Python Flask
• LlamaIndex
• Google Gemini AI
• BeautifulSoup4 (for web scraping)

Prerequisites

• Node.js (Latest LTS version)
• Python 3.8+
• Gemini API key
• Pinecone account and API key
• Redis instance (for rate limiting)

Installation

1. Clone the repository:

git clone https://github.com/Dhruv-Varshney-developer/ask-racha.git
cd ask-racha

2. Install frontend dependencies:

cd askracha
npm install

3. Install backend dependencies:

cd backend
pip install -r requirements.txt

4. Set up environment variables:

For the frontend:

cd askracha
cp .env.example .env

For the backend:

cd askracha/backend
cp .env.example .env

Then edit the backend .env file and add your configuration:

# Gemini AI Configuration
GEMINI_API_KEY=your_gemini_api_key_here

# Pinecone Configuration
PINECONE_API_KEY=your_pinecone_api_key_here
PINECONE_ENVIRONMENT=your_pinecone_environment
PINECONE_INDEX_NAME=ask-racha

# Redis Configuration (for rate limiting)
REDIS_URL=your_redis_url_here
# Example: redis://localhost:6379 for local development
# Or use a cloud Redis service like Upstash, Redis Cloud, etc.

Vector Store Setup

This application uses Pinecone as the vector database for storing and retrieving document embeddings.

Setting up Pinecone

1. Create a Pinecone Account:

   • Go to Pinecone.io and sign up for a free account
   • Navigate to the API Keys section in your dashboard

2. Get Your API Key:

   • Copy your API key from the Pinecone dashboard
   • Note your environment (e.g., us-east-1-aws, gcp-starter)

3. Configure Environment Variables:

Add the following to your backend .env file:

PINECONE_API_KEY=your_pinecone_api_key_here
PINECONE_ENVIRONMENT=your_pinecone_environment
PINECONE_INDEX_NAME=ask-racha

Index Configuration

The application automatically creates and configures the Pinecone index on startup with the following settings:

• Dimension: 768 (matches the embedding model)
• Metric: Cosine similarity
• Index Name: ask-racha (configurable via PINECONE_INDEX_NAME)

The knowledge base is loaded automatically when the backend starts, so there's no need for manual indexing.

Running the Application

Development Mode

To run both frontend and backend concurrently:

npm run dev:full

Or run them separately:

Frontend:

npm run dev

Backend:

npm run backend

Production Build

1. Build the frontend:

npm run build

2. Start the production server:

npm run start

Available Scripts

• npm run dev - Start the Next.js development server with Turbopack
• npm run build - Build the application for production
• npm run start - Start the production server
• npm run lint - Run ESLint for code linting
• npm run backend - Start the Python backend server
• npm run dev:full - Run both frontend and backend concurrently

Project Structure

ask-racha/
├── askracha/                    # Main application directory
│   ├── backend/                 # Python Flask backend
│   │   ├── app.py              # Main Flask application
│   │   ├── rag.py              # RAG implementation with Pinecone
│   │   ├── chat_context.py     # Chat context management
│   │   ├── document_scheduler.py # Document update scheduler
│   │   ├── requirements.txt    # Python dependencies
│   │   ├── storage/            # Vector store implementation
│   │   │   ├── __init__.py
│   │   │   └── pinecone_vector_store.py
│   │   ├── rate_limit/         # Rate limiting module
│   │   │   └── rate_limiter.py
│   │   ├── repos/              # Documentation repositories
│   │   │   └── setup-repos.sh # Script to clone docs
│   │   └── .env.example        # Backend environment template
│   │
│   ├── bot/                    # Discord bot (optional)
│   │   ├── bot.py             # Discord bot implementation
│   │   ├── api_client.py      # API client for backend
│   │   ├── config.py          # Bot configuration
│   │   ├── requirements.txt   # Bot dependencies
│   │   └── .env.example       # Bot environment template
│   │
│   ├── src/                   # Frontend source code
│   │   ├── app/              # Next.js app directory
│   │   ├── components/       # React components
│   │   ├── hooks/            # Custom React hooks
│   │   ├── types/            # TypeScript type definitions
│   │   └── middleware.ts     # Next.js middleware
│   │
│   ├── package.json          # Frontend dependencies
│   ├── next.config.ts        # Next.js configuration
│   ├── tailwind.config.ts    # Tailwind CSS configuration
│   ├── tsconfig.json         # TypeScript configuration
│   └── .env.example          # Frontend environment template
│
├── README.md                 # This file
├── LICENSE                   # Project license
└── .gitignore               # Git ignore rules

License

This project is licensed under the terms of the license included in the repository.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.