TelegramRAG: AI-Powered Document Analysis with Web and Telegram Interfaces

TelegramRAG is a Retrieval-Augmented Generation (RAG) application with a React frontend and a TON-gated Telegram bot, powered by LangChain, OpenAI, and Redis.

🎬 Live Demo

Watch a live demo of the project on YouTube

About The Project

TelegraRAG is a powerful tool for analyzing documents using artificial intelligence. It offers two convenient interfaces: a user-friendly web application and a Telegram bot for on-the-go analysis.

The core of the project is a RAG pipeline that can process Google Docs and PDF files, extract their content, and answer questions based on the information within them. This is made possible by leveraging the power of LangChain for orchestration, OpenAI for language understanding, and Redis for efficient vector storage and retrieval.

A unique feature of this project is the Telegram bot's integration with the TON (The Open Network) blockchain. To access the bot's full capabilities, users are required to make a payment in TON, demonstrating a practical application of cryptocurrency in a real-world application. This not only showcases technical skill in Web3 but also highlights a focus on privacy and decentralized systems.

Key Features:

• Dual Interface: Access the service through a modern React-based web app or a convenient Telegram bot.
• RAG Pipeline: Efficiently processes and analyzes documents to provide accurate answers to user queries.
• TON Payment Integration: A unique, privacy-focused payment system for the Telegram bot.
• Modular Architecture: A clean and maintainable codebase with a clear separation of concerns between the frontend, backend, and bot.

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Built With

• Backend: Python, Flask, LangChain, OpenAI, Redis
• Frontend: React, TypeScript, Styled-Components
• Bot: python-telegram-bot
• Database: Redis (for vector storage)
• Deployment: (Add your deployment platforms, e.g., Vercel, Heroku)

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Architecture

The application is composed of three main components:

1. Backend (Flask): A Python-based server that exposes a REST API for indexing documents, asking questions, and managing the vector store.
2. Frontend (React): A single-page application that provides a web interface for interacting with the backend API.
3. Telegram Bot: A Python script that connects to the Telegram API, handles user commands, and communicates with the backend API.

Getting Started

To get a local copy up and running, follow these simple steps.

Prerequisites

• Python 3.8+
• Node.js v14+
• Redis

Installation

1. Clone the repo

   git clone https://github.com/hazzsaeedharis/Telegram_RAG.git
   cd Telegram_RAG

2. Backend Setup

   # Install Python dependencies
   pip install -r requirements.txt
   
   # Create a .env file in the root directory and add your API keys
   cp .env.example .env

   Your .env file should look like this:

   OPENAI_API_KEY="your_openai_api_key"
   TELEGRAM_BOT_TOKEN="your_telegram_bot_token"
   TON_WALLET_ADDRESS="your_ton_wallet_address"
   BACKEND_URL="http://localhost:5001"
   

3. Frontend Setup

   # Navigate to the frontend directory
   cd frontend
   
   # Install NPM packages
   npm install

Running the Application

1. Start Redis
2. Start the Backend Server

   python app.py

3. Start the Frontend Development Server

   cd frontend
   npm start

4. Start the Telegram Bot

   python telegram_bot.py

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Usage

Web App

1. Open your browser and navigate to http://localhost:3000.
2. Paste the URL of a Google Doc or PDF into the input field and click "Index Document".
3. Once the document is indexed, you can ask questions about it in the second input field.

Telegram Bot

1. Start a conversation with your bot on Telegram.
2. Use the /start and /help commands to get information about the bot.
3. To unlock the bot's features, send a TON payment to the specified wallet address.
4. Use the /pay <transaction_hash> command to verify your payment.
5. Once verified, you can send a Google Docs link or upload a file to be indexed.
6. Ask questions about the indexed document.

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Future Improvements

• Implement a more robust payment verification system for the TON payments.
• Add support for more document types (e.g., .docx, .txt).
• Develop a user authentication system for the web app.
• Improve the conversational abilities of the Telegram bot.
• Deploy the application to a cloud platform for public access.