In the current era, artificial intelligence (AI) has become an integral part of advanced systems. From customer services to data analysis, AI-based tools play a crucial role in automating and improving complex processes. One of these tools is chatbots, which use AI to respond to users, process requests, and leverage reinforcement learning. This article explores a professional chatbot project using AI and reinforcement learning techniques. The project incorporates concepts such as Sentiment Analysis, Named Entity Recognition (NER), and enhanced security to prevent security attacks.
This chatbot system not only manages user interactions but also dynamically learns and provides continuous improvements using advanced algorithms like A3C, PPO, and DQN. The goal of this article is to provide a comprehensive guide to the project along with all related code so that enthusiasts can use this project as a foundation for larger projects.
Note: Artificial intelligence was used in the preparation of this article.
1. Sentiment Analysis and Named Entity Recognition (NER)
In this project, advanced BERT models are used so that the system can automatically analyze user sentiments and recognize key entities such as names of people, places, organizations, etc. Sentiment analysis allows the system to detect positive, negative, and neutral sentiments and generate appropriate responses based on them.
2. Reinforcement Learning
Reinforcement learning algorithms have been utilized to enhance the chatbot system’s performance. Algorithms like A3C, PPO, and DQN allow the chatbot to learn from its interactions with users and provide optimized performance. These algorithms learn based on past behaviors and continuously optimize the system.
3. Advanced Security
In this project, security is considered a priority. An advanced firewall has been implemented to prevent XSS and SQL Injection attacks. This firewall checks user inputs and prevents any security attacks, making the chatbot system more secure and resistant to various threats.
The final project is structured in a way that different modules are separated, allowing each part to be managed independently:
project-root/
│
├── src/
│ ├── api/
│ │ ├── apiRoutes.js
│ │
│ ├── chat/
│ │ ├── gptClient.js
│ │
│ ├── conversation/
│ │ ├── conversationService.js
│ │
│ ├── feedback/
│ │ ├── feedbackService.js
│ │
│ ├── learning/
│ │ ├── reinforcementLearning.js
│ │ ├── environments.js
│ │ ├── worker.js
│ │ ├── workerPool.js
│ │
│ ├── messaging/
│ │ ├── kafka.js
│ │
│ ├── metrics/
│ │ ├── metrics.js
│ │
│ ├── utils/
│ │ ├── preprocessor.js
│ │ ├── firewall.js
│ │
│ ├── database/
│ │ ├── models/conversationModel.js
│ │ ├── models/userModel.js
│
├── config/
│ ├── mongoConfig.js
│ ├── redisConfig.js
│ ├── passport.js
│
├── logs/
│ ├── error.log
│ ├── combined.log
│
├── tests/
│ ├── gptClient.test.js
│ ├── reinforcementLearning.test.js
│
├── server.js
├── .env
├── package.json
└── .gitignore
1. preprocessor.js (Sentiment Analysis and NER using advanced BERT models):
const { pipeline } = require('@huggingface/transformers');
// BERT model for sentiment analysis
const sentimentAnalyzer = pipeline('sentiment-analysis');
// BERT model for NER
const nerModel = pipeline('ner', { grouped_entities: true });
const analyzeSentiment = async (input) => {
const result = await sentimentAnalyzer(input);
return result[0]; // Sentiment analysis result
};
const extractEntities = async (input) => {
const entities = await nerModel(input);
return entities.map(entity => `${entity.word} (${entity.entity})`); // Extract entities
};
const preprocessInput = async (input) => {
const sentimentResult = await analyzeSentiment(input);
const entities = await extractEntities(input);
const processedInput = `${input} [Sentiment: ${sentimentResult.label}, Score: ${sentimentResult.score}] [Entities: ${entities.join(', ')}]`;
return processedInput;
};
module.exports = { preprocessInput };
2. firewall.js (Advanced firewall for preventing XSS and SQL Injection attacks):
const isSafeInput = (input) => {
const unsafePatterns = [
/<script/i,
/<[^>]+>/i,
/DROP\s+TABLE/i,
/UNION\s+SELECT/i,
/iframe/i,
/base64/i,
];
return !unsafePatterns.some(pattern => pattern.test(input)); // Prevent dangerous patterns
};
module.exports = { isSafeInput };
3. gptClient.js (Integration with GPT using Circuit Breaker and security):
const axios = require('axios');
const { circuitBreaker } = require('resilience4j');
const { preprocessInput } = require('../utils/preprocessor');
const firewall = require('../utils/firewall');
const retry = require('async-retry');
const breakerOptions = {
failureRateThreshold: 50,
waitDurationInOpenState: 10000,
ringBufferSizeInHalfOpenState: 5,
ringBufferSizeInClosedState: 10
};
const breaker = circuitBreaker(axios, breakerOptions);
const sendToChatGPT = async (userInput) => {
return retry(async (bail) => {
const cleanInput = await preprocessInput(userInput);
if (!firewall.isSafeInput(cleanInput)) {
return 'Your input is invalid or suspicious for security attacks.';
}
const headers = {
'Authorization': `Bearer ${process.env.GPT_API_KEY}`,
'Content-Type': 'application/json'
};
const data = {
model: 'gpt-4',
prompt: cleanInput,
max_tokens: 300,
temperature: 0.7
};
try {
const response = await breaker.fire({
method: 'post',
url: 'https://api.openai.com/v1/completions',
data,
headers,
timeout: 5000
});
return response.data.choices[0].text; // GPT response
} catch (error) {
if (error.response && error.response.status === 500) {
bail(new Error('GPT service failed, no retry.'));
}
throw error;
}
}, { retries: 3, factor: 2 });
};
module.exports = sendToChatGPT;
4. reinforcementLearning.js (A3C, PPO, and DQN algorithms with optimized architecture):
const tf = require('@tensorflow/tfjs-node');
const { runWorker } = require('./workerPool');
const ConversationEnvironment = require('./environments');
const stateSize = 10;
const actionSize = 4;
const learningRate = 0.0005;
const ppoSteps = 10;
const epsilon = 0.2;
const createActorNetwork = () => {
const model = tf.sequential();
model.add(tf.layers.dense({ units: 256, activation: 'relu', inputShape: [stateSize] }));
model.add(tf.layers.dense({ units: actionSize, activation: 'softmax' }));
return model;
};
const createCriticNetwork = () => {
const model = tf.sequential();
model.add(tf.layers.dense({ units: 256, activation: 'relu', inputShape: [stateSize] }));
model.add(tf.layers.dense({ units: 1 }));
return model;
};
// PPO optimization
const updatePolicyWithPPO = async (actorModel, criticModel, states, actions, rewards) => {
const oldProbs = await actorModel.predict(states);
for (let step = 0; step < ppoSteps; step++) {
const newProbs = await actorModel.predict(states);
const advantages = calculateAdvantages(rewards, states, criticModel);
const ratio = newProbs.div(oldProbs);
const clippedRatio = ratio.clipByValue(1 - epsilon, 1 + epsilon);
const loss = tf.min(ratio.mul(advantages), clippedRatio.mul(advantages));
await actorModel.fit(states, loss);
}
};
// DQN optimization
const createDQNNetwork = () => {
const model = tf.sequential();
model.add(tf.layers.dense({ units: 256, activation: 'relu', inputShape: [stateSize] }));
model.add(tf.layers.dense({ units: actionSize, activation: 'linear' }));
model.compile({ optimizer: tf.train.adam(learningRate), loss: 'meanSquaredError' });
return model;
};
const dqnTrain = async (model, states, actions, rewards, nextStates, done) => {
const qValues = model.predict(states);
const nextQValues = model.predict(nextStates);
const target = qValues;
const action = actions[0]; // Assigning action for first operation
target[action] =
done ? rewards : rewards + learningRate * Math.max(nextQValues);
await model.fit(states, target);
};
module.exports = { updatePolicyWithPPO, createActorNetwork, createCriticNetwork, createDQNNetwork, dqnTrain };
5. workerPool.js :
const { Worker } = require('worker_threads');
const os = require('os');
const numWorkers = os.cpus().length 2; // افزایش تعداد کارگران
const workers = [];
for (let i = 0; i < numWorkers; i++) {
workers.push(new Worker('./worker.js'));
}
const runWorker = (workerData) => {
return new Promise((resolve, reject) => {
const availableWorker = workers.find(worker => worker.threadId !== null);
if (!availableWorker) {
reject('No available workers');
}
availableWorker.postMessage(workerData);
availableWorker.once('message', (result) => {
resolve(result);
});
availableWorker.once('error', (error) => {
reject(error);
});
availableWorker.once('exit', (code) => {
if (code !== 0) {
reject(new Error(Worker stopped with exit code ${code}));
} else {
resolve(Worker finished successfully with exit code ${code});
}
});
});
};
module.exports = { runWorker };
6. apiRoutes.js
const express = require('express');
const router = express.Router();
const conversationController = require('../conversation/conversationService');
const feedbackController = require('../feedback/feedbackService');
// مدیریت مکالمات
router.post('/conversation/start', conversationController.startConversation);
router.post('/conversation/send', conversationController.sendMessage);
router.get('/conversation/history', conversationController.getConversationHistory);
// مدیریت بازخوردها
router.post('/feedback/submit', feedbackController.submitFeedback);
// هندلینگ خطاها
router.use((err, req, res, next) => {
console.error(err.stack);
res.status(500).json({ error: 'Internal Server Error' });
});
module.exports = router;
7. conversationService.js
const Conversation = require('../database/models/conversationModel');
const GPTClient = require('../chat/gptClient');
// شروع مکالمه
exports.startConversation = async (req, res) => {
try {
const newConversation = await Conversation.create({ userId: req.body.userId });
res.status(201).json(newConversation);
} catch (err) {
res.status(500).json({ error: 'Failed to start conversation' });
}
};
// ارسال پیام
exports.sendMessage = async (req, res) => {
try {
const response = await GPTClient(req.body.message);
await Conversation.updateOne({ _id: req.body.conversationId }, { $push: { messages: req.body.message } });
res.status(200).json({ response });
} catch (err) {
res.status(500).json({ error: 'Failed to send message' });
}
};
// دریافت تاریخچه مکالمات
exports.getConversationHistory = async (req, res) => {
try {
const history = await Conversation.find({ userId: req.query.userId });
res.status(200).json(history);
} catch (err) {
res.status(500).json({ error: 'Failed to retrieve conversation history' });
}
};
8. feedbackService.js
const Feedback = require('../database/models/feedbackModel');
exports.submitFeedback = async (req, res) => {
try {
const newFeedback = await Feedback.create({
userId: req.body.userId,
conversationId: req.body.conversationId,
feedback: req.body.feedback
});
res.status(201).json(newFeedback);
} catch (err) {
res.status(500).json({ error: 'Failed to submit feedback' });
}
};
9. environments.js
class ConversationEnvironment {
constructor() {
this.state = {
sentiment: null,
entities: [],
messages: []
};
}
reset() {
this.state = {
sentiment: null,
entities: [],
messages: []
};
}
step(action) {
// اعمال اکشن بر روی محیط
this.state.messages.push(action.message);
return this.state;
}
getState() {
return this.state;
}
}
module.exports = ConversationEnvironment;
10. worker.js
const { parentPort } = require('worker_threads');
const ConversationEnvironment = require('./environments');
const environment = new ConversationEnvironment();
parentPort.on('message', (message) => {
const state = environment.step(message.action);
parentPort.postMessage({ result: state });
});
11. kafka.js
const { Kafka } = require('kafkajs');
const kafka = new Kafka({
clientId: 'chatbot-client',
brokers: ['kafka:9092']
});
const producer = kafka.producer();
const consumer = kafka.consumer({ groupId: 'chatbot-group' });
const sendMessage = async (topic, message) => {
await producer.connect();
await producer.send({
topic,
messages: [{ value: message }]
});
await producer.disconnect();
};
const receiveMessages = async (topic, ) => {
await consumer.connect();
await consumer.subscribe({ topic, fromBeginning: true });
await consumer.run({
eachMessage: async ({ message }) => {
onMessage(message.value.toString());
}
});
};
module.exports = { sendMessage, receiveMessages };
12. metrics.js
const Prometheus = require('prom-client');
const requestCounter = new Prometheus.Counter({
name: 'api_requests_total',
help: 'Total number of API requests',
labelNames: ['method', 'endpoint']
});
const trackRequest = (req, res, next) => {
requestCounter.inc({ method: req.method, endpoint: req.path });
next();
};
module.exports = { trackRequest };
13. mongoConfig.js
const mongoose = require('mongoose');
const connectToMongo = async () => {
try {
await mongoose.connect(process.env.MONGO_URI, {
useNewUrlParser: true,
useUnifiedTopology: true
});
console.log('Connected to MongoDB');
} catch (err) {
console.error('Failed to connect to MongoDB', err);
process.exit(1);
}
};
module.exports = connectToMongo;
14. redisConfig.js
const Redis = require('ioredis');
const redis = new Redis({
host: process.env.REDIS_HOST,
port: process.env.REDIS_PORT,
password: process.env.REDIS_PASSWORD
});
redis.on('connect', () => {
console.log('Connected to Redis');
});
redis.on('error', (err) => {
console.error('Failed to connect to Redis', err);
});
module.exports = redis;
15. passport.js
const passport = require('passport');
const { Strategy: JwtStrategy, ExtractJwt } = require('passport-jwt');
const User = require('../database/models/userModel');
const opts = {
jwtFromRequest: ExtractJwt.fromAuthHeaderAsBearerToken(),
secretOrKey: process.env.JWT_SECRET
};
passport.use(
new JwtStrategy(opts, async (jwt_payload, done) => {
try {
const user = await User.findById(jwt_payload.id);
if (user) {
return done(null, user);
} else {
return done(null, false);
}
} catch (err) {
return done(err, false);
}
})
);
module.exports = passport;
This project is an example of an advanced AI-based chatbot with reinforcement learning. It successfully improves user interactions using advanced models like BERT and reinforcement algorithms such as A3C, PPO, and DQN.
