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What lead Jina AI CEO Han Xiao to Neural Search?

Connor Shorten17 June 2026Watch on YouTube

What you'll learn

  • Neural search uses embeddings to understand and search content at a semantic level
  • The granularity of embeddings determines which level of detail is captured and how precisely search queries can be executed
  • Preprocessing of embeddings is essential to enable different types of searches and applications
  • Embeddings can be applied across multiple use cases, from text to image search

Frequently asked questions

What is neural search and how is it different from traditional search?
Neural search uses machine learning models and embeddings to understand content semantically rather than relying on keyword matching alone. This enables finding relevant results based on meaning and context.
What is the granularity of embeddings and why does it matter?
Granularity refers to the level of detail captured in embeddings. It determines how precisely search queries can be executed and what information is available for different applications.
What role does preprocessing play in neural search?
Preprocessing of embeddings makes it possible to properly prepare and structure input data so that different search queries and applications can function effectively.
Can neural search be used for different types of search applications?
Yes, embeddings can be applied to text, images and other content types. How embeddings are preprocessed determines which applications are possible, from text search to image search.

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Description from the channel

This video explains one of the biggest lessons for me in interviewing Han Xiao from Jina AI. I hope this was a good explanation of the preprocessing / granularity of embeddings and how that can enable different kinds of search applications. Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos Chapters 0:00 Introduction