Machine Learning — Vector Database: COMPARE and UNDERSTAND
<p>In today’s digital age, databases are the cornerstone of nearly every application, from email clients to complex Enterprise Resource Planning (ERP) systems. Traditional database architectures like SQL, NoSQL, and Graph databases have their merits, but what about applications that need to simulate human-like thinking and respond in real time? This is where Vector Databases come into play, replicating the speed and intuition with which humans respond to questions.</p>
<h1>The Human Touch: How We Respond to Questions</h1>
<p>When someone asks us a question, such as “What is AWS?”, our brain doesn’t default to a textbook definition. Instead, it performs a rapid scan through a mental “database” of experiences, memories, and acquired knowledge. After this scan, we formulate a customized answer that we believe is most relevant to the question. In a nutshell, our brain “queries,” “retrieves,” and “generates” a tailored response almost instantaneously.</p>
<h1>Vector Databases: Mimicking the Human Mind</h1>
<p>Vector databases operate on a similar principle. They store information as mathematical vectors that capture the essence of the data. This vectorized information is then stored in a persistent layer. When a query is posed to the system, it’s also converted into a vector form, which is then matched against the existing database. The closest match, as measured by metrics such as cosine similarity, is then retrieved as the most appropriate answer.</p>
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