Vector Search

Vector search focuses on efficiently retrieving data points (vectors) most similar to a given query vector, a crucial task in numerous applications like information retrieval and recommendation systems. Current research emphasizes improving both the accuracy and speed of these searches, exploring advanced indexing techniques, optimized algorithms (like quantization methods and those leveraging hardware acceleration such as FPGAs), and the integration of vector search with relational databases and large language models. This field is vital for handling the massive datasets generated by modern AI applications, impacting areas ranging from improved search engine performance to more efficient knowledge retrieval in large language models.