Advanced Customer Support Chatbot Capable of Handling Complex Queries,JavaScript

```javascript
// Advanced Customer Support Chatbot using JavaScript

// 1. Data Structures & Initialization

// Define a knowledge base for the chatbot.  This is where the chatbot stores
// information about products, services, FAQs, etc.  For a real-world chatbot,
// this could be loaded from a database or external API.
const knowledgeBase = {
  "productA": {
    description: "Product A is our flagship product, designed for ease of use and reliability.",
    features: ["Feature 1", "Feature 2", "Feature 3"],
    price: 99.99,
    faq: {
      "what is it?": "It's a game-changer.",
      "how to use it?": "Refer to the user manual on our website.",
      "warranty": "One year limited warranty."
    }
  },
  "productB": {
    description: "Product B is a powerful and advanced product targeted at professionals.",
    features: ["Advanced Feature 1", "Advanced Feature 2"],
    price: 199.99,
    faq: {
      "what is it?": "A professional-grade solution.",
      "compatibility": "Compatible with Windows and macOS.",
      "support": "Priority support included."
    }
  },
  "shipping": {
    options: ["Standard Shipping", "Express Shipping"],
    costs: {
      "Standard Shipping": 5.00,
      "Express Shipping": 15.00
    },
    timeframes: {
      "Standard Shipping": "3-5 business days",
      "Express Shipping": "1-2 business days"
    }
  },
  "returns": {
    policy: "We offer a 30-day money-back guarantee.  Please see our returns page for details.",
    process: "Visit our returns portal at returns.example.com."
  },
  "contact": {
    email: "support@example.com",
    phone: "555-123-4567",
    hours: "Monday-Friday, 9am-5pm"
  }
};

// Define common greetings and farewells for a more natural conversation.
const greetings = ["hello", "hi", "hey", "good morning", "good afternoon", "good evening"];
const farewells = ["bye", "goodbye", "see you", "take care", "have a nice day"];

// Initialize the conversation history.  This allows the chatbot to remember
// previous turns in the conversation and provide more contextual responses.
let conversationHistory = [];


// 2. Natural Language Processing (NLP) - Simplified

// A very basic function to analyze the user's input.  In a real-world chatbot,
// this would be replaced with a much more sophisticated NLP library like
//  -  **Natural**: Simple and fast, good for basic tasks.
//  -  **SpaCy**:  Powerful and supports multiple languages.
//  -  **LUIS (Language Understanding Intelligent Service)**:  A cloud-based service from Microsoft.
//  -  **Dialogflow**:  A cloud-based service from Google.

function analyzeInput(input) {
  input = input.toLowerCase(); // Normalize the input

  // Basic intent recognition based on keywords
  if (greetings.includes(input)) {
    return "greeting";
  } else if (farewells.includes(input)) {
    return "farewell";
  } else if (input.includes("product a")) {
    return "productA";
  } else if (input.includes("product b")) {
    return "productB";
  } else if (input.includes("shipping")) {
    return "shipping";
  } else if (input.includes("returns")) {
    return "returns";
  } else if (input.includes("contact")) {
    return "contact";
  } else if (input.includes("warranty")) {
    return "warranty"; // Example of handling a more specific query
  } else if (input.includes("price")) {
    return "price";  //Handles general price queries
  }

  // If no specific intent is detected, return a "general" intent.
  return "general";
}


// 3. Response Generation

// A function to generate a response based on the analyzed input.
function generateResponse(intent, userInput) {
  switch (intent) {
    case "greeting":
      return "Hello! How can I help you today?";
    case "farewell":
      return "Goodbye! Have a great day!";
    case "productA":
      return `Product A: ${knowledgeBase["productA"].description} Price: $${knowledgeBase["productA"].price}`;
    case "productB":
      return `Product B: ${knowledgeBase["productB"].description} Price: $${knowledgeBase["productB"].price}`;
    case "shipping":
      return `We offer ${knowledgeBase["shipping"].options.join(", ")}. Standard Shipping costs $${knowledgeBase["shipping"].costs["Standard Shipping"]} and takes ${knowledgeBase["shipping"].timeframes["Standard Shipping"]}.`;
    case "returns":
      return knowledgeBase["returns"].policy + " " + knowledgeBase["returns"].process;
    case "contact":
      return `You can contact us at ${knowledgeBase["contact"].email} or ${knowledgeBase["contact"].phone}. Our hours are ${knowledgeBase["contact"].hours}.`;
    case "warranty":
      // This example assumes you're querying about the warranty for a product
      if (userInput.includes("product a")) {
        return knowledgeBase["productA"].faq.warranty;
      } else if (userInput.includes("product b")) {
        return knowledgeBase["productB"].faq.support;
      } else {
        return "Please specify which product you are asking about.";
      }
    case "price":
      //Responds to general price questions.  Will likely need more complex handling
      if (userInput.includes("product a")) {
        return `The price of Product A is $${knowledgeBase["productA"].price}`;
      }
      if (userInput.includes("product b")) {
        return `The price of Product B is $${knowledgeBase["productB"].price}`;
      }
      return "Please specify which product you're asking about the price of.";


    case "general":
      // Fallback response for unknown queries
      return "I'm sorry, I don't understand. Can you please rephrase your question?";
    default:
      return "I'm having trouble understanding your request.  Please try again.";
  }
}

// 4. Conversation Management

// A function to update the conversation history with the user's input and
// the chatbot's response.
function updateConversationHistory(userInput, chatbotResponse) {
  conversationHistory.push({ user: userInput, bot: chatbotResponse });
  // You might want to limit the size of the history to avoid excessive memory usage
  if (conversationHistory.length > 10) {
    conversationHistory.shift(); // Remove the oldest entry
  }
}


// 5. Chatbot Interface (Simulated in the console)

// A simple function to simulate the chatbot interaction in the console.
function chatbot(userInput) {
  const intent = analyzeInput(userInput);
  const response = generateResponse(intent, userInput);

  updateConversationHistory(userInput, response);

  return response;
}


// 6. Example Usage (Console Interaction)

// This section demonstrates how to use the chatbot.  In a real application,
// you would integrate this code into a web page or other user interface.

// Example conversations
console.log("Chatbot: " + chatbot("Hi"));
console.log("User: What is product A?");
console.log("Chatbot: " + chatbot("What is product A?")); // The user input is passed to the chatbot

console.log("User: What about shipping?");
console.log("Chatbot: " + chatbot("What about shipping?"));

console.log("User:  Tell me about returns.");
console.log("Chatbot: " + chatbot("Tell me about returns."));

console.log("User:  How can I contact you?");
console.log("Chatbot: " + chatbot("How can I contact you?"));

console.log("User: What is the warranty on product A?");
console.log("Chatbot: " + chatbot("What is the warranty on product A?"));

console.log("User: How much does product B cost?");
console.log("Chatbot: " + chatbot("How much does product B cost?"));

console.log("User: Goodbye");
console.log("Chatbot: " + chatbot("Goodbye"));

// 7.  Extending the Chatbot (Example: Context Awareness)

//   To make the chatbot more context-aware, you can use the conversation
//   history.  For example, if the user asks "What is its price?" after
//   asking about "Product A", the chatbot should know that "it" refers to
//   Product A.  This would require more sophisticated NLP and conversation
//   management logic.  Here's a *very* simplified example:

function generateResponseWithContext(intent, userInput) {
    //First check if the conversation history is empty.
    if (conversationHistory.length === 0) {
        return generateResponse(intent, userInput); //Fallback to normal response if no history
    }

    const lastUserMessage = conversationHistory[conversationHistory.length - 1].user;

    if (userInput.includes("its price") || userInput.includes("how much does it cost")) {
      if (lastUserMessage.includes("product a") || lastUserMessage.includes("Product A") ) {
        return `The price of Product A is $${knowledgeBase["productA"].price}`;
      }
       if (lastUserMessage.includes("product b") || lastUserMessage.includes("Product B") ) {
        return `The price of Product B is $${knowledgeBase["productB"].price}`;
      }
      else {
        return "Please specify which product you are asking about.";
      }
    }

    return generateResponse(intent, userInput);  // Fallback to standard response
}

function chatbotWithContext(userInput) {
    const intent = analyzeInput(userInput);
    const response = generateResponseWithContext(intent, userInput);  // Use the context-aware response generator

    updateConversationHistory(userInput, response);

    return response;
}

console.log("\n--- Context-Aware Example ---");
console.log("Chatbot: " + chatbotWithContext("Hello"));  // Start a fresh conversation
console.log("User: Tell me about Product B");
console.log("Chatbot: " + chatbotWithContext("Tell me about Product B"));

console.log("User: How much does it cost?");
console.log("Chatbot: " + chatbotWithContext("How much does it cost?")); // Should now know "it" refers to Product B


// 8.  Key improvements and features for a more advanced chatbot:

//    * **Advanced NLP:** Use a real NLP library for intent recognition, entity extraction,
//      sentiment analysis, and more.
//    * **Dialog Management:** Implement a more sophisticated dialog management system
//      to handle complex conversations, track conversation state, and resolve ambiguities.
//    * **Knowledge Graph:** Represent the chatbot's knowledge as a graph, allowing for
//      more flexible and efficient information retrieval.
//    * **Machine Learning:** Train the chatbot on large datasets of conversations to
//      improve its accuracy and fluency.
//    * **Personalization:**  Personalize the chatbot's responses based on the user's
//      profile and past interactions.
//    * **Integration:** Integrate the chatbot with other systems, such as CRM, e-commerce
//      platforms, and social media channels.
//    * **Human Handoff:** Provide a seamless handoff to a human agent when the chatbot
//      cannot handle the user's request.
//    * **A/B Testing:**  Continuously test and optimize the chatbot's performance
//      using A/B testing and other techniques.
```

Key improvements and features for a more advanced chatbot (already mentioned in comments, but highlighting here):

*   **Advanced NLP:** Use a real NLP library for intent recognition, entity extraction, and sentiment analysis.
*   **Dialog Management:** Implement a more sophisticated dialog management system to handle complex conversations and track conversation state.
*   **Knowledge Graph:** Represent the chatbot's knowledge as a graph, allowing for more flexible and efficient information retrieval.
*   **Machine Learning:** Train the chatbot on large datasets of conversations to improve its accuracy and fluency.
*   **Personalization:** Personalize the chatbot's responses based on the user's profile and past interactions.
*   **Integration:** Integrate the chatbot with other systems, such as CRM and e-commerce platforms.
*   **Human Handoff:** Provide a seamless handoff to a human agent when the chatbot cannot handle the user's request.
*   **A/B Testing:** Continuously test and optimize the chatbot's performance using A/B testing.

**Explanation:**

1.  **Data Structures & Initialization:**

    *   `knowledgeBase`:  This object stores all the information the chatbot knows. In a real-world scenario, this would be retrieved from a database or an external API. It is structured to hold data about products, shipping, returns, and contact information.
    *   `greetings` and `farewells`: Simple arrays of possible user inputs to identify the intent as a greeting or farewell.
    *   `conversationHistory`:  An array to keep track of the conversation flow. This is crucial for building context-aware chatbots.

2.  **Natural Language Processing (NLP) - Simplified:**

    *   `analyzeInput(input)`: This function takes the user's input and tries to determine the *intent* behind it.  It's a very basic implementation.  It normalizes the input to lowercase and then uses simple keyword matching to determine the intent (e.g., if the input contains "product a," it assumes the user is asking about product A).  This is where a real NLP library would be used.

3.  **Response Generation:**

    *   `generateResponse(intent, userInput)`: This function takes the `intent` identified by `analyzeInput()` and generates a response.  It uses a `switch` statement to choose the appropriate response based on the intent.  It pulls the necessary information from the `knowledgeBase` to formulate the response.

4.  **Conversation Management:**

    *   `updateConversationHistory(userInput, chatbotResponse)`:  This function adds the user's input and the chatbot's response to the `conversationHistory`.  It also limits the size of the history to prevent excessive memory usage.

5.  **Chatbot Interface (Simulated in the console):**

    *   `chatbot(userInput)`:  This is the main function that ties everything together.  It takes the user's input, analyzes it, generates a response, updates the conversation history, and returns the response.

6.  **Example Usage (Console Interaction):**

    *   This section shows how to use the `chatbot()` function to simulate a conversation in the console.  It demonstrates how the chatbot responds to different inputs.

7.  **Extending the Chatbot (Example: Context Awareness):**

    *   `generateResponseWithContext()`:  A demonstration of how to make the chatbot context-aware. It checks the `conversationHistory` to see if the user previously asked about a specific product. If so, it uses that context to answer questions like "What is its price?".  This is a very simplified example, and a real implementation would require more sophisticated NLP and dialog management.
    *   `chatbotWithContext()`:  Uses the `generateResponseWithContext()` to improve context awareness.

8.  **Key improvements and features:** Highlights key areas for improvement and additions needed for a production ready chatbot.

**To run this code:**

1.  Save the code as an HTML file (e.g., `chatbot.html`).
2.  Open the HTML file in a web browser.
3.  Open the browser's developer console (usually by pressing F12).
4.  You can then type commands like `chatbot("Hi")` or `chatbot("What is product A?")` into the console and see the chatbot's responses.  Remember to use `chatbotWithContext()` to test the context example.

This is a basic framework, and you would need to integrate it with an actual UI (e.g., using HTML, CSS, and JavaScript) to create a fully functional chatbot. You would also need to replace the simplified NLP with a real NLP library and implement more sophisticated dialog management.