In under 2 minutes, we explain what vector embeddings are, how they work, and how to use them in real-world applications like text expansion. We'll also show how Elasticsearch supports vector search with two powerful models: E5, open-source text embedding models designed for multilingual search, and ELSER, a sparse embeddings model from Elastic. [Video] Quick Question - Full Episodes → https://www.youtube.com/playlist?list=PLhLSfisesZIunW575Av3nmLIw7p5ujCN0 [Free training] Semantic search text embedding → https://www.elastic.co/training/semantic-search-text-embedding?utm_source=organic-social&utm_medium=youtube&utm_campaign=quick-questions-cee-gc&utm_content=ep6 00:00 Quick Questions in the Age of AI 00:15 What are vector embeddings? (numerical meaning of data) 00:40 Example: dog vs retriever vs cookies 01:00 Embedding models vs vector databases explained 01:20 Popular embedding models: E5 and ELSER 01:49 Elastic resources Learn more about vector embeddings: https://www.elastic.co/what-is/vector-embedding Learn more about Elasticsearch: https://www.elastic.co/elasticsearch Start the 14-day trial for free! No credit card required: https://cloud.elastic.co/registration Subscribe to Elastic’s Community YT channel: https://www.youtube.com/c/OfficialElasticCommunity Connect with us on social media: LinkedIn: https://www.linkedin.com/company/elastic-co X: https://twitter.com/elastic Facebook: https://www.facebook.com/elastic.co About Elastic Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale. Elastic’s solutions for search, observability, and security are built on the Elastic Search AI Platform — the development platform used by thousands of companies, including more than 50% of the Fortune 500. #elasticsearch #ElasticObservability #ElasticSecurity

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