TOR and ChatGPT Integration: Building an Anonymized Text-Based Conversation Application
Abstract: This article proposes a novel idea of combining the TOR network with ChatGPT, an AI language model, to develop a text-based conversation application. By leveraging ChatGPT’s capabilities, the application can analyze user inputs effectively. Simultaneously, by integrating TOR, the application gains access to .onion sites and extracts valuable text content from these sources. This scraped data then becomes instrumental in training ChatGPT, enabling it to generate data-informed responses to user inquiries. As a result, this integration offers a distinctive and secure approach to engage in anonymous and private text-based conversations.
Keywords: TOR network, ChatGPT, AI language model, Text-based conversation application, User inputs, .onion sites, Data scraping, Data-informed responses, Anonymous conversations, Private text-based communication, Secure integration, Novel idea, AI capabilities, Text content extraction, Training ChatGPT, Distinctive approach, Data privacy, Text analysis, Secure communication.
Introduction:
The idea behind this project is to explore the potential of integrating the TOR network with ChatGPT, a powerful AI language model. This combination allows for the creation of a text-based conversation application that prioritizes security and anonymity. ChatGPT, developed by OpenAI, serves as the language model, producing human-like text responses based on given prompts. By incorporating the TOR network, users can interact with ChatGPT anonymously, ensuring their identity remains hidden. Moreover, the integration enables the application to access restricted .onion sites, providing a unique data collection capability. This opens up new possibilities for secure and private text-based conversations with the assistance of AI.
Leveraging ChatGPT API for Text-Based Conversations:
The first step in creating the application involves utilizing the ChatGPT API provided by OpenAI. The getChatGPTResponse(prompt) function is responsible for taking user input and sending it to the ChatGPT API to generate a continuation for the given text. The API response contains a continuation that is used to form the basis for the application's reply.
// Implementing ChatGPT API Integration
async function getChatGPTResponse(prompt) {
// ... Code for connecting to ChatGPT API and getting the response ...
return data.choices[0].text.trim();
}Establishing Anonymity with TOR Network:
To ensure user anonymity, the application connects to the TOR network using the tor-request library. TOR is an anonymous network that allows users to access .onion sites and protect their identities. By setting up a TOR connection, the application ensures that user requests are routed through the network.
// Implementing TOR Network Connection
function connectToTor() {
return new Promise((resolve, reject) => {
torRequest.TorControlPort.port = 9051; // Set the port for the Tor control port
// Authenticate with the Tor control port if needed
torRequest.TorControlPort.authenticate('YOUR_TOR_CONTROL_PASSWORD', (err) => {
if (err) {
console.error('Error authenticating with Tor control port:', err);
reject(err);
} else {
console.log('Connected to Tor network.');
resolve();
}
});
});
}Searching and Scraping .onion Sites:
To obtain relevant data for training ChatGPT, the application searches .onion sites using the searchOnion(query) function. This function makes a search request to the DuckDuckGo site over TOR using HTTP proxy and retrieves the search results.
// Searching .onion Sites
async function searchOnion(query) {
// ... Code for making search request using TOR ...
return data;
}After searching .onion sites, the application extracts text content using web scraping with the scrapeOnionSites(chatGPTResponse) function. The content is then used to train the ChatGPT model.
// Scraping .onion Sites
async function scrapeOnionSites(chatGPTResponse) {
// ... Code for web scraping .onion sites ...
return scrapedText;
}Training ChatGPT with Scraped Data:
The scraped text data is appended to a training file, and the ChatGPT model is updated with the trainChatGPT(scrapedText) function.
// Training ChatGPT
async function trainChatGPT(scrapedText) {
// ... Code for appending scrapedText to training data and updating ChatGPT ...
return chatGPT; // Return the trained ChatGPT instance
}Generating Responses with Trained ChatGPT:
Finally, the answerQueryWithTrainedChatGPT(userInput, chatGPT) function takes user input, processes it through the trained ChatGPT model, and returns the response.
// Generating Responses with Trained ChatGPT
async function answerQueryWithTrainedChatGPT(userInput, chatGPT) {
// ... Code for generating responses using trained ChatGPT ...
return chatGPTResponse;
}Conclusion:
The integration of the TOR network and ChatGPT facilitates the creation of a unique text-based conversation application that ensures user anonymity and generates data-driven responses. By leveraging the power of ChatGPT and the security of TOR, this application provides users with a private and personalized conversational experience.
Note: The implementation code provided is a conceptual representation and may require further development and testing to create a fully functional application. Additionally, ensure compliance with legal and ethical considerations when using the TOR network and web scraping techniques.
Just a test code to give an idea and to think about how it could be if improved
// Import necessary libraries
const fetch = require('node-fetch'); // Assuming you have 'node-fetch' installed
const fs = require('fs');
const { ChatGPT } = require('chatgpt');
const torRequest = require('tor-request');
const socks = require('socks');
// Step 1: Initialize ChatGPT API
const apiKey = 'YOUR_API_KEY';
const apiEndpoint = 'https://api.openai.com/v1/engines/davinci-codex/completions';
// Step 2: Send API Request to ChatGPT
async function getChatGPTResponse(prompt) {
try {
const response = await fetch(apiEndpoint, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
Authorization: `Bearer ${apiKey}`,
},
body: JSON.stringify({
prompt: prompt,
max_tokens: 150,
stop: '\n',
}),
});
const data = await response.json();
return data.choices[0].text.trim();
} catch (error) {
console.error('Error fetching ChatGPT API:', error);
return 'Error: Unable to fetch response from ChatGPT API.';
}
}
// Step 3: Implement Tor Integration
function connectToTor() {
return new Promise((resolve, reject) => {
torRequest.TorControlPort.port = 9051; // Set the port for the Tor control port (default is 9051)
// Authenticate with the Tor control port if needed (only if you have set a password for your Tor control port)
torRequest.TorControlPort.authenticate('YOUR_TOR_CONTROL_PASSWORD', (err) => {
if (err) {
console.error('Error authenticating with Tor control port:', err);
reject(err);
} else {
console.log('Connected to Tor network.');
resolve();
}
});
});
}
// Step 4: Implement .onion Search using DuckDuckGo with Tor
async function searchOnion(query) {
try {
// Check if Tor connection is successful
await connectToTor();
// Make a search request to DuckDuckGo over Tor using HTTP Proxy
const torOptions = {
proxy: {
ipaddress: '127.0.0.1', // Change this to the IP address of your Tor proxy
port: 9050, // Change this to the port of your Tor proxy
type: 5, // SOCKS version (5 for Tor)
},
};
const response = await fetch(`http://3g2upl4pq6kufc4m.onion/html?q=${encodeURIComponent(query)}`, {
method: 'GET',
headers: {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3',
},
agent: new socks.HttpAgent(torOptions.proxy),
});
const data = await response.text();
return data;
} catch (error) {
console.error('Error searching .onion site:', error);
return 'Error: Unable to make .onion search using DuckDuckGo over Tor.';
}
}
// Step 5: Scrape Text Content from .onion Sites
async function scrapeOnionSites(chatGPTResponse) {
try {
const onionSearchResult = await searchOnion(chatGPTResponse);
// Implement web scraping to extract text content from onionSearchResult
// and return the scraped text.
return onionSearchResult;
} catch (error) {
console.error('Error scraping .onion sites:', error);
return 'Error: Unable to scrape text content from .onion sites.';
}
}
// Step 6: Train ChatGPT with Scraped Text Data
async function trainChatGPT(scrapedText) {
try {
// Append the scraped text data to the training file
fs.appendFileSync('training_data.txt', scrapedText);
// Reinitialize ChatGPT with updated training file
const chatGPT = new ChatGPT({
apiKey: 'YOUR_CHATGPT_API_KEY',
model: 'gpt-3.5-turbo',
// Use 'training_data.txt' as the training data file
trainingFile: 'training_data.txt',
});
// Train ChatGPT with new data
await chatGPT.train();
console.log('ChatGPT trained with updated data.');
return chatGPT; // Return the trained ChatGPT instance
} catch (error) {
console.error('Error training ChatGPT:', error);
return null; // Return null if there is an error in training
}
}
// Step 7: Answer the Initial Query using Trained ChatGPT
async function answerQueryWithTrainedChatGPT(userInput, chatGPT) {
try {
// Get response from ChatGPT using trained model
const chatGPTResponse = await chatGPT.generate(userInput);
// Return the response from ChatGPT
return chatGPTResponse;
} catch (error) {
console.error('Error generating response with trained ChatGPT:', error);
return 'Error: Unable to generate response with trained ChatGPT.';
}
}
// Example Usage:
async function main() {
try {
// Assuming userInput is the user's initial query
const userInput = 'How does photosynthesis work?';
// Step 1: Analyze the query with ChatGPT
const chatGPTResponse = await getChatGPTResponse(userInput);
// Step 2: Shorten, search, and scrape .onion sites for relevant information
const scrapedText = await scrapeOnionSites(chatGPTResponse);
// Step 3: Train ChatGPT with the scraped text data
const chatGPT = await trainChatGPT(scrapedText);
// Step 4: Answer the initial query using trained ChatGPT
const answer = await answerQueryWithTrainedChatGPT(userInput, chatGPT);
console.log('Answer:', answer);
} catch (error) {
console.error('Error in main:', error);
}
}
main();
