Generative AI and Vector Databases

Generative AI, which is a hot trend in AI and machine learning, involves using machine learning models to generate content based on given input. This concept emerged in the mid-2010s when researchers explored using machine learning to understand language and create predictive models. They developed vector embeddings, which were trained models that outputted vector representations of words that could be closely related to each other in sentences. The key innovation was the ability to make relations between words and sentences, which led to better search and recommendation systems. The combination of vector databases like Weeviante, as an effective database for storing and retrieving vectorized information, with generative models like JetGPT, has enabled the technique known as retrieval augmented generation (RAG). This approach allows for more advanced search and recommendation capabilities, making it possible to generate content based on retrieved information from databases. The future of vector databases may involve storing and managing weights of models, as well as the potential integration of these databases with AI models to provide real-time data manipulation and retrieval.