LLM-Powered Voice based Booking Chatbot

Here, we’ll explore the LLM Powered Voice Booking Chatbot and how it transforms customer interactions with intelligent voice-based automation.

Large language models (LLM) have significantly improved since their introduction, demonstrating proficiency in text production, summarization, and reasoning.

But the usage and integration of tools is the greatest advancement in the field of LLMs. By analyzing user queries and producing functional calls, LLMs can employ tools to activate functions.

The versatility of LLMs, when combined with tools, opens up new avenues for application across various sectors, from healthcare and education to finance and entertainment.

A stronger focus on tool integration will shape the next era of AI(Artificial Intelligence). It will support better collaboration between humans and AI systems, leading to more advanced and efficient solutions.

What is an LLM-Powered Voice based Booking Chatbot?

Customers anticipate a flawless experience everywhere, from making hotel reservations to reserving a seat at a well-known restaurant.

Here come LLM-powered Voice based booking chatbots: cutting-edge chatbots that improve customer service while streamlining the reservation process.

They allow users to have voice based interaction with the system for better customer satisfaction and engagement.

These chatbots can engage in natural conversations, understand user intent, and provide personalized recommendations based on individual preferences.

These chatbots are capable of managing bookings for hotels, flights, restaurants, and events, all while improving customer engagement.

How to Build LLM-Powered Voice based Booking Chatbot

1) Speech to text:

• Building a Voice based AI Chatbot, start with Speech recognition and Speech to text Generation.
• There are different approaches available for speech to text generation like utilization of JavaScript Speech Recognition or using OpenAI’s Whisper model.
• By utilizing speech recognition, you can convert user voice to text query.

2) Tool Creation and Integration:

• There are different ways of creating tool function. Depending upon LLM provider, you have different ways of creating and binding tools, or you can utilize langchain_core library for creating tools.
• For tool creation, you have to write a create a function which has to be called when a condition is met, For example: If a user request for booking a room, “book_room” function should be called.
• These functions execute CRUD operations on the database and make the necessary changes as per requested in the user call
• This function must include a proper doc string that explains the action it performs, the required arguments, and their type and format.
• You can utilize Langchain_core ‘@tool’ decorator over the function as show in the example.

@tool
def book_the_room(checkin_date, checkout_date, room_no):
    """
    This function processes requests for booking, confirming, or reserving a room.
    Parameters:
    - checkin_date (str): The desired check-in date in the format "DD-MM-YYYY".
    - checkout_date (str): The desired check-out date in the format "DD-MM-YYYY".
    - room_no (str): The room numbers you provided follow a specific format: "WB" followed by a two-digit number. for example: "WB03"

    Returns:
    - str: A success message if the booking is confirmed, or a failure message if the request is invalid or the dates are incorrect.
    """
yours book room functioanlity here...

• After creating all the required tools, create a list of tools function name. For example, tools = [rooms_details, book_room, check_user_booking]
• Use bind_tools method on your LLM instance. For example, tool_llm = llm.bind_tools(tools)

3) Creating Prompt and invoking tool_llm with user query:

• Now, We will create a prompt for our Tool_llm describing its task to perform and other important details. This prompt contains guidelines for strict formatting and functional calling.
• After creation of prompt, you can utilize Langchain invoke method for function calling as shown below:

chat = (
        "{"
        + f"query: {query}, system_prompt: {TOOL_PROMPT}"
        + "}"
    )
messages = [HumanMessage(chat)]
ai_msg = tool_llm.invoke(messages)
messages.append(ai_msg)
for tool_call in ai_msg.tool_calls:
        selected_tool = {
            "rooms_details": rooms_details,
            "book_room": book_room,
            "check_user_booking": check_user_booking
        }[tool_call["name"].lower()]
        tool_output = selected_tool.invoke(tool_call["args"])
        messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"]))
result = tool_output

• The function call made by the LLM model produced the response.

4) Utilizing LLM to generate User friendly response:

• The tool response is sent to LLM along with user query.
• The Langchain chain invoke method can be used to call the LLM. LLM generates a user friendly response in text format.
• Use prompts to guide the LLM in generating responses. For instance, you can specify a tone (friendly, professional) or a format (bullet points, paragraphs) to enhance clarity and engagement.

5) Text to Speech:

• The generated response can be in any form, perform operations on it to convert into string. These string can be converted into audible audio with the help of Text to speech software and libraries.
• One of the best Texts to Speech instrument out there is JavaScript Speech Synthesis.
• JavaScript Speech Synthesis is, indeed, a powerful feature built into modern web browsers. It allows developers, therefore, to convert text into spoken words, ultimately creating an engaging user experience.
• JavaScript Speech Synthesis, in fact, utilizes the Web Speech API for generating audio outputs from raw text. Specifically, it employs the SpeechSynthesisUtterance constructor to create a new utterance object
• The Speak method starts speaking the text defined in the SpeechSynthesisUtterance object.

Conclusion

Building an LLM-powered voice-based booking chatbot represents a significant advancement in enhancing customer service and streamlining reservation processes.

By harnessing speech recognition and text to speech generation, we create a unique, engaging user experience which provides the user a seamless booking experience.

The LLM functional call feature has significantly streamlined the process for users, making it easy to book and cancel reservations through simple chat interactions.

Consequently, this approach eliminates the need for cumbersome methods like clicking through multiple pages or sending emails.

Ultimately, the LLM-powered voice-based booking chatbot significantly enhances user experience by providing a more efficient and, moreover, engaging solution for booking and reservation processes.