How neural hashing releases the potential of AI retrieval | Algolia

Searching involves three distinct processes: query understanding, retrieval, and ranking. Retrieval is the most vital for improving overall search quality. Machine learning AI has been applied to query processing and ranking but not to retrieval until recently. Vector search, a machine learning technology for AI search, greatly improves retrieval by determining relevance for any particular query through vectors. Hybrid search combines vector and keyword search technologies, offering the best results for customers. Neural hashing is a technique that allows for compressing vectors without losing information, making it as fast to deliver as keyword search while reducing manual workload associated with improving search relevance.

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