Enhancing Communication in Global Markets: Leveraging PGVector for Multilingual Semantic Search, Llama2-Powered RAG Systems, and State-of-the-Art Translation Models to Optimize Multilingual Customer Interactions
This post was co-authored with Rafael Guedes.
As organizations keep evolving, there is one thing that remains constant: the pursuit of customer satisfaction. Enhancing customer experience is one of the most critical aspects of building a sustainable and successful business. The integration of AI in companies’ workflows will revolutionize this arena. It will enable personalized customer service, allowing businesses to meet, anticipate, and surpass customer expectations. Companies embracing AI for customer service early will gain a significant competitive edge.
Envision a situation where you are browsing Amazon for a specific product. Upon reaching the product’s detailed page, you face the crucial task of deciding its suitability for your needs. To do this, you begin sifting through thousands of customer reviews written in several different languages — a task that is tedious, challenging, and time-consuming. But, imagine if you had access to a chatbot capable of addressing your queries in your language. It would be using insights drawn from other customers’ feedback. This could significantly streamline everyone’s decision-making process.
In this article, we provide a detailed explanation of how multilingual translation models like mBART work and its implementation in Python. We also show how we can adapt a pre-trained multilingual model to perform language detection in a sequence of text. Finally, we create a chatbot powered by multilingual semantic search, an RAG system, and a translation model to answer customers in their language based on other customers’ product reviews.
As always, the code is available on our GitHub.