Vector Database

Vector databases are specialized systems designed for efficient storage and retrieval of high-dimensional data, primarily vector embeddings generated by machine learning models. Current research emphasizes optimizing search speed and accuracy through techniques like quantization, dimensionality reduction (e.g., using Fast Fourier Transforms), and novel indexing structures tailored for multi-tenant environments and mixed vector-relational queries. These advancements are crucial for improving the performance of applications such as large language models, image processing, and geospatial AI, where rapid similarity search is essential for effective knowledge retrieval and analysis.