Navigating Neural Search: Avoiding Common Pitfalls

Jo Kristian Bergum • Location: TUECHTIG • Back to Haystack EU 2023

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.

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Jo Kristian Bergum


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