Optimizing Vector Search

Mansi Tibude, an electronics engineer, introduces the concept of vector search, an AI-powered search method superior to traditional text-based search. Vector search enables results from various data types like audio, video, and images, contrasting with standard search engines. ElasticSearch particularly excels by employing hybrid search, blending semantic and vector search for faster, more accurate outcomes. The core of vector search involves converting queries and documents into vectors, storing them, and using mathematical functions for efficient matching. This process is optimized using models like KNN and RAG, with vector databases crucial for storing high-dimensional data. Vertical scaling, involving hardware upgrades such as increased CPU cores, and horizontal scaling through node and shard increases contribute to speed improvements. Real-world applications like Docusign highlight vector search's effectiveness in managing vast amounts of data and delivering relevant results. This approach can be extended to include handwritten text and artworks. By combining KNN and CNN algorithms with NLP, the modified architecture enables diverse input formats and context extraction. Vector and semantic searches revolutionize search by managing numerous queries efficiently, providing faster results and contextual understanding.