How Vector Databases are Revolutionizing Unstructured Data Search in AI Applications

Vector databases are revolutionizing unstructured data search in AI applications by enabling efficient and semantically meaningful retrieval of relevant information. They store and search data based on semantic similarity rather than exact matches, allowing for more nuanced and context-aware information retrieval. Applications of vector databases include retrieval-augmented generation (RAG), recommender systems, molecular similarity search, and multimodal similarity search. These databases are transforming various fields by providing a unified way to represent and search across different types of data.