n this session, we will delve into the dynamic relationship between large language models (LLMs) and vector databases. LLMs, like ChatGPT, have the ability to generate human-like text and respond intelligently to questions. But how do these models understand and maintain context? The key lies in vectorization. Text is transformed into high-dimensional mathematical representations—vectors—that capture semantic meaning beyond individual words. These vectors allow LLMs to efficiently store, compare, and retrieve information. By utilizing vector databases, we can perform fast similarity searches, enabling AI systems to find the most relevant information, even when it isn’t an exact match to the input. This capability is crucial for tasks such as contextual search, recommendation systems, and natural language understanding. During the session using Kotlin and LangChain4j, I will demonstrate how to convert text into vectors, store them in a database, and retrieve relevant information in real-time.