Your task is to develop a Node.js chatbot application using the ChatGPT API embeddings, LangChain JS, and FaissStore. 

### Specifications:

1. **Environment**:
   - Use Node.js.
   - Utilize LangChain JS for text data handling.
   - Use FaissStore for the vector database.

2. **Functionality**:
   - When the application starts, it should automatically trigger a training process that reads PDF or TXT files from a `train_files` folder.
   - Generate embeddings for the training data using the ChatGPT API.
   - Store the training results (embeddings) in a `training_results` folder.
   - Use these stored training results for responding to user queries via the ChatGPT API.

3. **File Structure**:
   ```
   my-chatbot/
   ├── train_files/       # Folder containing PDFs and TXT files for training
   ├── training_results/  # Folder to store training embeddings
   ├── src/
   │   ├��─ index.js       # Main entry point for the app
   │   ├── train.js       # Script for training from files
   │   └── chatbot.js     # Chatbot logic
   ├── package.json       # Node.js dependencies
   └── README.md          # Project documentation
   ```

4. **Code Requirements**:
   - Code should be modular, with clear separation of concerns.
   - Include comments for clarity.
   - Use efficient techniques for parsing and training with large text files.

### Example Code:

**src/index.js**:
```javascript
const express = require('express');
const { trainBot } = require('./train');
const { chatBot } = require('./chatbot');

const app = express();
const PORT = process.env.PORT || 3000;

// Training the chatbot when the app starts
console.log("Training the bot, please wait...");
trainBot().then(() =&gt; {
  console.log("Training complete. Chatbot is ready!");

  app.use(express.json());

  // Endpoint to handle chat requests using ChatGPT API
  app.post('/chat', async (req, res) =&gt; {
    const { message } = req.body;
    const response = await chatBot(message);
    res.json({ response });
  });

  app.listen(PORT, () =&gt; {
    console.log(`Server is running on port ${PORT}`);
  });
});
```

**src/train.js**:
```javascript
require('dotenv').config();
const fs = require('fs');
const path = require('path');
const { Configuration, OpenAIApi } = require("openai");
const faissStore = require('faissstore');

// Load and parse training files
async function loadTrainingFiles() {
  const dirPath = path.join(__dirname, '../train_files');
  const files = fs.readdirSync(dirPath);
  
  const trainingData = [];
  
  for (const file of files) {
    const filePath = path.join(dirPath, file);
    const content = fs.readFileSync(filePath, 'utf-8');
    trainingData.push(content);
  }
  
  return trainingData;
}

// Generate embeddings using OpenAI's ChatGPT
async function generateEmbeddings(texts) {
  const configuration = new Configuration({
    apiKey: process.env.OPENAI_API_KEY, // Set your OpenAI API key in a .env file
  });
  const openai = new OpenAIApi(configuration);

  const embeddings = [];
  for (const text of texts) {
    const embeddingResponse = await openai.createEmbedding({
      model: 'text-embedding-ada-002', // Example embedding model
      input: text,
    });
    embeddings.push(embeddingResponse.data.data[0].embedding);
  }

  return embeddings;
}

// Train the bot with loaded files and save embeddings
async function trainBot() {
  const trainingData = await loadTrainingFiles();
  
  const embeddings = await generateEmbeddings(trainingData);
  const vectorStore = new faissStore.VectorStore(); // Initialize FaissStore

  await vectorStore.addVectors(embeddings);

  const resultPath = path.join(__dirname, '../training_results');
  if (!fs.existsSync(resultPath)) {
    fs.mkdirSync(resultPath);
  }

  // Store vectors in the result path
  vectorStore.save(resultPath);
}

module.exports = { trainBot };
```

**src/chatbot.js**:
```javascript
require('dotenv').config();
const faissStore = require('faissstore');
const path = require('path');
const { Configuration, OpenAIApi } = require("openai");

// Load trained vectors and initialize FaissStore
async function chatBot(query) {
  const resultPath = path.join(__dirname, '../training_results');
  const vectorStore = await faissStore.VectorStore.load(resultPath);

  const queryEmbedding = await generateEmbedding(query);
  const results = await vectorStore.query(queryEmbedding);
  const relevantResponse = results[0]; // Taking the most relevant response for simplicity

  // Setup OpenAI API client
  const configuration = new Configuration({
    apiKey: process.env.OPENAI_API_KEY, // Set your OpenAI API key in a .env file
  });
  const openai = new OpenAIApi(configuration);

  // Fetch a response from ChatGPT
  const chatResponse = await openai.createChatCompletion({
    model: "gpt-3.5-turbo", // You can customize the model
    messages: [{ role: "user", content: relevantResponse }]
  });

  return chatResponse.data.choices[0].message.content;
}

async function generateEmbedding(text) {
  const configuration = new Configuration({
    apiKey: process.env.OPENAI_API_KEY,
  });
  const openai = new OpenAIApi(configuration);
  
  const embeddingResponse = await openai.createEmbedding({
    model: 'text-embedding-ada-002',
    input: text,
  });
  return embeddingResponse.data.data[0].embedding;
}

module.exports = { chatBot };
```

**Notes**:
- Create a .env file in the root directory of your project and add your OpenAI API key: `OPENAI_API_KEY=your_api_key`.
- Ensure all necessary modules (`dotenv`, `express`, `openai`, `faissstore`, and `fs`) are installed by running `npm install`.