Automate Customer Support: GPT Chatbot for Hotels (PART1)

Streamlined Customer Support Operations: Leveraging GPT models like ChatGPT can significantly improve the efficiency of your customer service departments. The AI-driven chatbot can handle multiple customer queries simultaneously, reducing waiting times and improving customer experience.

24/7 Availability: Unlike human operators, GPT models are available round-the-clock. They assure that your customers' queries are addressed promptly any time of the day.

Cost-Effective Solution: By automating routine customer interactions, you can reduce the load on your customer service team and save on operational costs. It's a one-time investment that pays long-term dividends.

Scalability: With GPT models, service scalability is not a concern. Whether you get 10 queries or 1000, ChatGPT handles it all without compromising on service quality.

Multilingual Capabilities: GPT models can work in multiple languages, making it an invaluable tool for hotels with global clientele. You can deliver superior customer support in the language that your customers prefer.

ChatGPT and other large language models are remarkable for their ability to generate human-like responses and handle a wide range of topics and tasks. However, using ChatGPT directly for creating a customer support solution may not be as straightforward as it seems.

It's important to realize that despite their intelligent responses, these models fundamentally do not understand context or possess intrinsic knowledge, they can only mimic understanding based on the extensive data they were trained on. This often leads them to generate responses that seem plausible at first glance but are nonsensical or incorrect upon inspection. For a customer support scenario, this could potentially lead to incorrect advice being given, causing customer dissatisfaction or, in worst-case scenarios, operational disruption.

Additionally, while ChatGPT can handle a wide range of topics, it lacks the specific knowledge and context of your business and your customers' specific needs and queries.** Out-of-the-box, it may not understand your company’s products, services, or culture, thus the responses may not be consistent with your brand.**

Lastly, large language models like ChatGPT can sometimes produce unpredictable and uncontrollable results, which is not ideal for a customer support environment where accuracy and reliability are paramount.

So, while tools like ChatGPT serve as a powerful foundation, customization and supervision are necessary to ensure these AI models effectively address your customer support needs, maintain brand consistency, and ultimately increase customer satisfaction.

There are essentiall 2 methods to tailor the chatbot and mitigate the hallucination problem: the Embedding Route and the Fine-tuning Route.

The Embedding Route entails infusing your model with knowledge from external databases during the encoding process. This route grants the chatbot accessibility to a vast reservoir of factual knowledge, drastically reducing the chances of generating hallucinated responses.

On the other hand, the Fine-tuning Route involves the modification of a model's parameters post-training, allowing you to apply a sharp focus on crucial aspects of the conversation. Through continuous feedback, the model is adjusted to align more closely with human conversation, eliminating hallucinated responses over time. This route grants you the control to personalize the chatbot according to the unique requirements of each context.

But which one is more effective? And can we combine them? When you are looking to build a customer support chatbot for a hotel, each route has its benefits.

For example, the Embedding Route is crucial in arming your chatbot with vital facts about the hotel, such as its amenities, location, rates, etc., which is often sought after by customers. However, this approach is data-demanding and may require a substantial update if any hotel information changes.

On the other hand, the Fine-tuning Route allows your chatbot to adapt to individual customer behaviours over time, and respond better to context-specific inquiries. It may lose some factual accuracy in return, but that's where the human support can step in and correct.

In reality, combining the benefits of both routes can offer a more balanced and effective solution. The Embedding Route can cover factual accuracy, while the Fine-tuning Route focuses on personalised interactions. Integration of both methods would enable the chatbot to offer reliable information while maintaining an empathetic and human-like conversational style, improving the overall user experience significantly. This would be an ideal approach to make your hotel's customer support chatbot more efficient and user-friendly.

Below, I will show you a simple example of how to build a blend model using ChatGPT as base language model, and the following are the ingredients of building a powerful chatbot to be able to integrate private data

• GPT-API, for leveraging the power of advanced language models like GPT-3 in making the chatbot more conversationally intelligent. You can find more information here, OpenAI
• Weaviate, to allow easy handling and exploration of vast amounts of data, useful for the chatbot's knowledge base. You can find more information here, Weaviate
• Langchain, for making the chatbot capable of understanding multiple languages, and thus extending its usability to non-English speakers. You can find more information here, Langchain
• NodeJS, for serving as the powerful yet lightweight runtime environment in which the chatbot will operate.

Let's dive straight into the coding part. First off, we need to set up Weaviate and form a connection to the database.

Initiating Weaviate Database

The first step in establishing the connection between Weaviate and your database involves setting up a connection string. You can simply configure one through Weaviate Cloud Services (WCS) available here: Quickstart for WCS

Once you've established the database, select "Details" to review them. For procuring the Weaviate API key, hit the "API keys" button and copy the subsequently displayed api key. Keep it safe as we will need it in the subsequent steps.

Client Installation and Connection Establishment

Proceed by incorporating weaviate-ts-client into your project with npm using the following command: npm install weaviate-ts-client

Substitute the endpoint address and API key with your unique details in the below script and run it for your preferred client:

With a successful setup, you will receive a response affirming that the server is ready and waiting. True

Once you receive this message, it denotes a successful connection to the database. The subsequent, equally important step involves injecting factual data into the database.

Once you receive this message, it denotes a successful connection to the database. The subsequent, equally important step involves injecting factual data into the database. The factual data for your hotel may include essential details like the hotel's name, address, and contact information, the number of rooms available, type of rooms (standard, deluxe, suite), amenities offered (like free breakfast, Wi-Fi, gym facilities), room rates, and special offers or discounts if any, and more.

Integrating such factual data can augment your database with valuable information which can be leveraged to provide an optimal customer service experience and ensure efficient hotel management.

To insert data into your database, here is an example: