Navigating Neural Search: Avoiding Common Pitfalls

Neural search, often known by various names, including semantic search, has reached a stage where it is primarily associated with learned vector representations of queries and documents. This dense representational method reduces the scoring process to a vector similarity function.

In this talk, we take a holistic approach and demystify the neural networks behind these vector representations - the text embedding models. We explore various open-source text embedding models, discussing choosing one by considering factors like language capabilities, task alignment, accuracy, and cost-effectiveness.

Finally, we look at embedding retrieval or vector search and how introducing approximate vector search can degrade the accuracy so much that significantly cheaper retrieval methods will be favorable.

Jo Kristian Bergum

Vespa

Jo Kristian is a Distinguished Engineer at Yahoo, where he spends his time working on the open-source Vespa.ai serving engine. Jo Kristian has 20 years of experience with deploying search systems at scale.