What does the rise of ChatGPT mean for customer support and vector databases?

Translator | Cui Hao​

Reviewer | Sun Shujuan

Opening

Most organizations think of customer service as an expense. In fact, customer service can also be viewed as an opportunity. It allows you to continue driving value for your customers after the sale. Successful businesses know that customer service not only retains customers but also increases business revenue. Customer service is an underrated tool that can enhance marketing and sales efforts through referrals, testimonials, and classic word-of-mouth. And, serving customers in real time and without delay is crucial. With the advent of artificial intelligence, this requirement has become achievable. ​

With artificial intelligence, it is possible to assist customers during their journey with issues that arise during the journey any problem. In addition, many real-life problems can be solved through artificial intelligence-led chatbots and machine learning (ML) capabilities, such as NLP and real-time data analysis. Finally, with the continued adoption of vector databases, businesses can leverage unstructured data to cater to customer needs. ​

Artificial Intelligence in Customer Support​

Interestingly, ever The first artificial intelligence chatbot for customer support was born in the 1960s when ELIZA, a psychologically intelligent Virtual assistant to help doctors with diagnosis and treatment. After that, it took a back seat. Until now, when customers demand instant gratification. According to Hubspot research, 90% of customers want an immediate response to their questions. Additionally, the report shows that 80% of customers will stop doing business with a service company after experiencing a bad experience. This highlights the importance of good customer service and being available to customers around the clock. Coincidentally, as ChatGPT shines on the global stage, we are able to witness a The emergence of an artificial intelligence-led customer service revolution. ​

The rise of ChatGPTChatGPT is hailed as a new turning point in the information age , it is an artificial intelligence-based platform that answers complex questions in a conversational manner. Built by OpenAI, it is designed and trained to understand and answer questions asked by humans. Therefore, ChatGPT breaks the boundaries of what is possible with conversational AI. ​

Definition of ChatGPT​

##ChatGPT: A revolution in the information age? ​

ChatGPT is an advanced chatbot built on GPT-3.5 that can talk to humans in the form of a conversation. It follows a Large Language Model (LLM), which is trained to accurately predict the next word in a sentence. While it looks like an autocomplete feature on your phone, ChatGPT does it on an unrealistic scale. Researchers learned that the more data it was exposed to, the better its conversational abilities became. ​

Some commercial use cases of ChatGPT are listed below:

​

Customer Service​ By training ChatGPT on numerous interactions with customers, you can automatically generate responses to the most frequently asked questions. ​

Text Generation​

You can create social media posts or product descriptions by submitting the appropriate query. ​

Sentiment Analysis​

You can monitor customer sentiment by analyzing the sentiment in feedback statements. ​

Conversational Artificial Intelligence​

It can quickly analyze patient data to recommend the correct diagnosis and treatment plan (a more advanced form of ELIZA). ​

Virtual Assistant​

ChatGPT makes it very easy to generate messages, emails or any content. ​

GPT-4 Incredible Wish​

When we understand ChatGPT capabilities, OpenAI has upgraded it in the form of GPT-4. While its predecessor had 175 billion parameters, GPT-4 is said to have 1 trillion parameters, making it incredibly fast and smart. ​

For every query you ask, GPT-4 will use 1 trillion parameters to processed to give the most accurate results. Although it hasn't been released yet, GPT-4 will cause a shocking shift in customer service. ​

ChatGPT’s customer service challenges​

ChatGPT based on the people it contacts information to respond to submitted inquiries. Therefore, when you use this tool to serve customers on your website without training them first, it will have limitations. Additionally, because it can only obtain information about your company from Internet-facing assets such as websites and other portals, the answers may not be accurate or helpful. ​

##ChatGPT’s limitations in serving customers

#The second limitation of ChatGPT is the inherent nature of customer inquiries. Most customer questions are vague and require logical translation to provide an appropriate answer. Unfortunately, ChatGPT hasn't mastered this art yet. ​

ChatGPT may not be fully capable of managing your customer service yet, but that shouldn’t stop you from applying AI to improve the customer experience. ​

Build an AI Customer Service Agent​

Many organizations limit artificial intelligence Strategy, in order to improve customer service, they use the engine to generate automated responses, but most of these responses are relatively generic. However, customers want personalized answers that better demonstrate professional capabilities, and they also have requirements for response times. You can provide accurate and on-demand customer experience by building a CS agent that uses NLP (Natural Language Processing) and NLU (Natural Language Understanding) to understand the context of customer queries. Then, by infusing it with search capabilities run by artificial intelligence, it can provide seamless human-like virtual conversations. ​

#The main challenge in delivering AI experiences is that companies have large amounts of unstructured data to manage and complex to analyze. This perception quickly changed with the advent of ChatGPT, although vector databases have been used to manage unstructured data before. ​

#The architecture shown below defines a seamless and efficient customer support agent workflow. ​

##Build an Artificial Intelligence Customer Support Agent ​

AI-based customer support involves two different processes - one is the indexing service and the other is the query service, represented by green and yellow respectively. Let's see how they work. ​

Indexing Service​

The Indexing Service transfers data to the knowledge that contains the document library and obtain data from the knowledge base. As each document in the knowledge base is added or changed, Embedding's API is activated to convert the new information into vectors. These vectors are then added to a vector database to facilitate fast semantic searches. ​

Query Service​

#Using the Query Service, you can provide a text query, in a process similar to indexing, Embeddings API will turn it into a vector. This vector is then used to search and match documents through the database and give the best results. Since the search engine has already prepared the file's vectors, it makes this process easy and fast, even for millions of files. ​

What is a vector database? ​

Vector databases store, index, and search entire unstructured data embedded in an ML (machine learning) model-driven manner. It effectively simplifies the data set, representing data objects as numerical values ​​so that they can be managed in a process called vector embedding. ​

#The vector database indexes these embeddings so that vectors can be compared to each other, or searched Vector comparison of queries. Vector database facilitates data management functions such as create, read, update and delete. Similarity search and metadata filtering are two other essential features of Vector Database, giving you comprehensive search capabilities. ​

Some examples of vector databases:​

• ##Qdrant: A similarity search engine and vector database that provides services such as storage, search and management of vectors through API, while achieving dynamic query planning and efficient loading of data indexes. Among other vector search engines, Qdrant is a powerful and scalable choice. ​
• Vertex: The Vertex artificial intelligence machine engine is built by Google and is a low-latency vector database. It organizes vectors according to unique aspects of their embedding to facilitate easy and scalable searches. ​
• NucliaDB：NucliaDB is an open source, cloud-native vector database and distributed search engine that allows you to store data on its cloud infrastructure. ​

Language Artificial Intelligence Service​

Applying artificial intelligence for language analysis is quickly becoming a trend in various industries. Various businesses are looking for use cases for artificial intelligence to decode text and gain valuable business insights. Text can be in written, spoken or visual format. You can leverage your unstructured data: text, speech, images, and videos to generate AI datasets and # and to smarten your ML algorithms and models. ​

Quite a few companies such as OpenAI, Cohere, and AI2Labs provide APIs that allow you to access the natural Advanced models for language applications. ​

The Future of Customer Support

Powered by emerging technologies, customer service is poised to take a giant leap forward, improving customer experience and Improve Ability to better support customers. Companies are looking to hone AI-based conversations by relying heavily on self-service platforms and chatbots to improve their knowledge base. Additionally, advances in NLP in recent years have made virtual assistance a seamless customer service tool. For example, chatbots can now conduct human-like conversations, requiring human intervention only in complex situations. ​

Translator Introduction

Cui Hao, 51CTO community editor, senior architect Teacher, has 18 years of experience in software development and architecture, and 10 years of experience in distributed architecture. ​

##Original title: ​​What ChatGPT Means for Customer Support and the Role of Vector Databases​​

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