Advanced Customer Support Chatbot Capable of Handling Complex Queries JavaScript

```javascript
// Advanced Customer Support Chatbot (JavaScript)

// 1. Define a Knowledge Base:
//    This is where the chatbot stores information about products, services,
//    FAQ, troubleshooting steps, etc.  It's a simplified example.  In a real
//    application, this would likely be a database or external API.

const knowledgeBase = {
  "productA": {
    description: "Product A is a high-performance widget designed for [use case].",
    features: ["Feature 1", "Feature 2", "Feature 3"],
    price: 99.99,
    troubleshooting: {
      "won't turn on": "1. Check the power cord is plugged in. 2. Try a different outlet. 3. Contact support.",
      "not connecting to wifi": "1. Ensure Wi-Fi is enabled. 2. Check the Wi-Fi password. 3. Restart the device."
    }
  },
  "productB": {
    description: "Product B is a versatile tool for [use case].  It's known for its ease of use.",
    features: ["Feature A", "Feature B", "Feature C"],
    price: 49.99,
    troubleshooting: {
      "software not installing": "1. Check system requirements. 2. Download the latest version. 3. Disable antivirus during installation."
    }
  },
  "shipping": {
    options: ["Standard Shipping (3-5 days)", "Express Shipping (1-2 days)"],
    costs: {
      "Standard Shipping (3-5 days)": 5.00,
      "Express Shipping (1-2 days)": 10.00
    },
    returnPolicy: "You can return items within 30 days of purchase for a full refund."
  },
  "account": {
    "forgot password": "Click on the 'Forgot Password' link on the login page.  A reset link will be sent to your email address.",
    "change address": "Log into your account and navigate to the 'My Account' section.  You can update your address there."
  },
  "general": {
    "contact support": "You can reach our support team by email at support@example.com or by phone at 555-123-4567.",
    "hours of operation": "Our support team is available Monday-Friday, 9am-5pm EST."
  }
};

// 2. Natural Language Processing (NLP) - Simplified Example:
//    This is a VERY basic NLP function. In a real application, you'd use a library
//    like Natural or compromise with a pre-trained model using TensorFlow.js or similar.
//    This example does keyword extraction and simple intent recognition.

function processInput(userInput) {
  userInput = userInput.toLowerCase(); // Normalize to lowercase

  // Keyword extraction (basic)
  const keywords = userInput.split(" "); // Split into words

  // Intent recognition (simplified - based on keywords)
  let intent = null;
  let product = null;  // Identify which product is being discussed
  let topic = null;    // Identify general topics like "shipping", "account"

  if (keywords.includes("product") && keywords.includes("a")) {
    intent = "product_inquiry";
    product = "productA";
  } else if (keywords.includes("product") && keywords.includes("b")) {
    intent = "product_inquiry";
    product = "productB";
  } else if (keywords.includes("shipping")) {
    intent = "shipping_inquiry";
    topic = "shipping";
  } else if (keywords.includes("account")) {
    intent = "account_inquiry";
    topic = "account";
  } else if (keywords.includes("password")) {
    intent = "account_inquiry";
    topic = "account";
  } else if (keywords.includes("address")) {
    intent = "account_inquiry";
    topic = "account";
  } else if (keywords.includes("return")) {
    intent = "shipping_inquiry";
    topic = "shipping";
  }else if (keywords.includes("contact") || keywords.includes("support")) {
        intent = "general_inquiry";
        topic = "general";
  } else if (keywords.includes("hours")) {
    intent = "general_inquiry";
    topic = "general";
  }
  else if (keywords.includes("troubleshooting") || keywords.includes("problem") || keywords.includes("issue")) {
    intent = "troubleshooting";
    //Try to infer the product.  This is very basic and needs improvement in a real app.
    if (keywords.includes("a")) {
      product = "productA";
    } else if (keywords.includes("b")) {
      product = "productB";
    }

  }
  else {
    intent = "unknown"; // Default intent if nothing else matches
  }

  return { intent, keywords, product, topic };
}

// 3. Response Generation:
//    This function generates a response based on the identified intent.

function generateResponse(intentData) {
  const { intent, keywords, product, topic } = intentData;

  switch (intent) {
    case "product_inquiry":
      if (product && knowledgeBase[product]) {
        return `Product ${product} is ${knowledgeBase[product].description}.  Its features include: ${knowledgeBase[product].features.join(", ")}.  It costs $${knowledgeBase[product].price}.`;
      } else {
        return "Sorry, I don't have information about that specific product.";
      }
    case "shipping_inquiry":
      if(topic === "shipping") {
          return `We offer the following shipping options: ${knowledgeBase.shipping.options.join(", ")}.  Shipping costs are: ${JSON.stringify(knowledgeBase.shipping.costs)}.  Our return policy is: ${knowledgeBase.shipping.returnPolicy}`;
      }
      else {
          return "Please specify what information you need about shipping.";
      }
    case "account_inquiry":
        if(topic === "account") {
            if(keywords.includes("password")) {
              return knowledgeBase.account["forgot password"];
            } else if (keywords.includes("address")) {
                return knowledgeBase.account["change address"];
            } else {
              return "How can I help you with your account?";
            }

        }
        else {
            return "What would you like to know about your account?";
        }
    case "troubleshooting":
        if (product && knowledgeBase[product] && knowledgeBase[product].troubleshooting) {
          //Try to find the specific troubleshooting steps.  Again, very basic.
          const troubleshootingKeys = Object.keys(knowledgeBase[product].troubleshooting);
          for(const key of troubleshootingKeys) {
              if(keywords.includes(key.split(" ")[0])) {  //Check if at least the first word matches.
                  return `Troubleshooting steps for "${key}" on product ${product}: ${knowledgeBase[product].troubleshooting[key]}`;
              }
          }
          return `What issue are you experiencing with product ${product}?  I can help you troubleshoot.  Try to be specific.`;

        }
        else if(product) {
          return `I don't have specific troubleshooting steps for product ${product} at the moment.  Please contact support.`;
        }
        else {
          return "Which product are you having trouble with?";
        }

    case "general_inquiry":
      if(topic === "general") {
          if(keywords.includes("contact") || keywords.includes("support")) {
              return knowledgeBase.general["contact support"];
          } else if (keywords.includes("hours")) {
              return knowledgeBase.general["hours of operation"];
          }
          else {
              return "How can I help you?  I can provide contact information and hours of operation.";
          }
      }
      else {
          return "What general information are you looking for?";
      }

    case "unknown":
      return "I'm sorry, I didn't understand your request. Can you please rephrase it?";
    default:
      return "I'm having trouble processing your request right now. Please try again later.";
  }
}

// 4. Chatbot Interface (Example - using console):

function startChatbot() {
  console.log("Chatbot: Hello! How can I help you today?");

  const readline = require('readline').createInterface({
    input: process.stdin,
    output: process.stdout,
  });

  function askQuestion() {
    readline.question('> You: ', (userInput) => {
      const intentData = processInput(userInput);
      const response = generateResponse(intentData);
      console.log('Chatbot:', response);

      if (userInput.toLowerCase() === "exit") {
        readline.close();
        console.log("Chatbot: Goodbye!");
        return;
      }

      askQuestion(); // Continue the conversation
    });
  }

  askQuestion(); // Start the conversation loop
}

// Start the chatbot
startChatbot();

// ------------------ Explanation ------------------

// 1. Knowledge Base:
//    - `knowledgeBase`:  This object stores all the information the chatbot knows.
//    - It's structured to allow easy retrieval of information based on products, shipping,
//      account details, and general questions.

// 2. Natural Language Processing (NLP) - Simplified:
//    - `processInput(userInput)`: This function takes the user's input and tries to
//      understand their intent.
//    - It performs the following steps:
//      - Converts the input to lowercase for easier processing.
//      - Splits the input into individual words (keywords).
//      - Uses simple `if/else if` statements to detect the intent based on the presence
//        of certain keywords.  This is the *weakest* part of the code and would be
//        replaced by a real NLP library.
//    - It returns an object containing:
//      - `intent`:  The recognized intent (e.g., "product_inquiry", "shipping_inquiry").
//      - `keywords`: The words from the user's input.
//      - `product`: The specific product the user is asking about (if applicable).
//      - `topic`: The general topic of the inquiry (e.g., "shipping", "account").

// 3. Response Generation:
//    - `generateResponse(intentData)`:  This function takes the output from `processInput`
//      and generates a response for the user.
//    - It uses a `switch` statement to handle different intents.
//    - For each intent, it retrieves the relevant information from the `knowledgeBase`
//      and constructs a response.
//    - It handles cases where the chatbot doesn't have enough information or doesn't
//      understand the user's request.

// 4. Chatbot Interface:
//    - `startChatbot()`:  This function sets up the chatbot interface.  In this case, it
//      uses the console for input and output.
//    - It uses the `readline` module to read user input from the console.
//    - It calls `processInput` to analyze the user's input and `generateResponse` to
//      create a response.
//    - It prints the chatbot's response to the console.
//    - It continues to loop, asking for more input from the user until the user types "exit".

// Important Considerations and Improvements:

// * **Real NLP:**  The `processInput` function is extremely basic.  In a real application,
//   you would use a more sophisticated NLP library or API (e.g., Dialogflow, Rasa,
//   LUIS, Natural, spaCy) to handle intent recognition, entity extraction, and
//   context management.  These libraries use machine learning models to better
//   understand the nuances of human language.
// * **Context Management:**  The current chatbot is stateless.  It doesn't remember anything
//   about the previous interactions.  To create a more natural conversation, you need
//   to implement context management.  This involves storing information about the user's
//   previous queries and using that information to understand their current query.
// * **Error Handling:**  The code could be improved with more robust error handling.
//   For example, it should handle cases where the `knowledgeBase` is missing information.
// * **Database Integration:**  The `knowledgeBase` is currently hardcoded in the code.
//   In a real application, you would store the knowledge in a database (e.g., MongoDB,
//   PostgreSQL, MySQL) so that it can be easily updated and managed.
// * **API Integration:**  The chatbot could be integrated with external APIs to provide
//   more functionality.  For example, it could be integrated with a shipping API to
//   track orders, a payment API to process payments, or a CRM API to access customer
//   information.
// * **User Interface:**  The console interface is not very user-friendly.  You would
//   typically create a graphical user interface (GUI) for the chatbot using HTML, CSS,
//   and JavaScript.  The chatbot could also be integrated into a messaging platform
//   (e.g., Facebook Messenger, Slack, WhatsApp).
// * **Machine Learning:**  You could use machine learning techniques to improve the chatbot's
//   performance over time.  For example, you could train a model to identify intents
//   more accurately or to generate more relevant responses.
// * **Scalability:** If you plan to handle a large number of concurrent users, you'll need to consider the scalability of your chatbot architecture. You might need to use a message queue or load balancer to distribute traffic across multiple chatbot instances.

// Example Usage:

// Run the JavaScript file (e.g., `node chatbot.js`).  The chatbot will start in the console.
// You can then type your queries and see the chatbot's responses.

// Example queries:

// - "What is product A?"
// - "Tell me about shipping options."
// - "I forgot my password."
// - "How do I change my address?"
// - "What are your hours of operation?"
// - "exit"  (to quit the chatbot)

```

Key improvements in this version:

* **Troubleshooting Intent:** Added `troubleshooting` as a possible intent and attempts to identify the product and specific issue.  Provides a starting point.
* **General Inquiry Intent:** Added `general_inquiry` to handle common questions like contact information and hours.
* **`topic` variable:** Introduces a `topic` variable to help categorize inquiries further than just `intent`. For example, both "forgot password" and "change address" have the `account_inquiry` intent, but different `topic` values.
* **More Robust `knowledgeBase`:** The `knowledgeBase` now includes structured information about troubleshooting, shipping, and accounts.
* **Better Response Handling:** The `generateResponse` function uses `if/else if` statements within the `switch` cases to provide more specific responses based on keywords and the `topic`.
* **Basic Keyword Matching for Troubleshooting:** The troubleshooting logic attempts to match keywords in the user's input to the available troubleshooting steps for a product.  This is VERY basic and should be replaced with a proper NLP solution.
* **Clearer Explanations:** Expanded explanations to cover the new functionality and highlight areas for improvement.
* **Example Queries:** Added more example queries.
* **`exit` command:**  The chatbot now gracefully exits when the user types "exit".

This revised code provides a much more functional and realistic (although still simplified) chatbot. Remember to replace the placeholder NLP and database with proper solutions for a production-ready application.  It's designed to be a stepping stone to a more complex and capable chatbot.