When it comes to storing arrays of floating-point numbers, you can make use of a vector database. Following that, you can search the database by employing a similarity function, which will return any vector that is comparable to another vector. This video will demonstrate how these databases function behind the scenes, as well as how you can use serverless Pinecone with just a few lines of code.
When the video creator started exploring vector databases, they found limited information on their internal workings. Most sources highlighted their importance, speed, and relevance to AI applications but didn’t delve into their mechanics. This video aims to clarify vector databases for those interested in their technical aspects. It offers a simple explanation of how they store and retrieve vast amounts of vectors efficiently, demonstrated with Python code.
The video covers Facebook’s FAISS library and Pinecone’s vector database, showcasing how they optimize search processes, with all code available in the description.
