Vector databases in LLMs and search
One of my first projects as a software developer was developing genetic analysis algorithms. We built software to scan electrophoresis samples into a database, and my job was to convert each DNA pattern’s image into representable data. I did this by converting the image into a vector, with each point representing the attributes of the sample. Once vectorized, we could store the information efficiently and calculate the similarity between DNA samples.
Converting unstructured information into vectors is commonplace today and used in large language models (LLMs), image recognition, natural language processing, recommendation engines, and other machine learning use cases.