Vector search is a textbook example for the benefit of Single Instructions Multiple Data (SIMD) because the whole process of comparing two vectors to see how similar they are to each other is performed by comparing each of their dimensions in one form or another. So, the same operation is repeated on each of the dimensions and that for each of the candidate vectors. Performing … [Read more...] about Elastic Platform 8.15: Leverage vector search optimizations and semantic text
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Secrets, Secrets Are No Fun. Secrets, Secrets (Stored in Plain Text Files) Hurt Someone
Secrets are meant to be hidden or, at the very least, only known to a specific and limited set of individuals (or systems). Otherwise, they aren't really secrets. In personal life, a secret revealed can damage relationships, lead to social stigma, or, at the very least, be embarrassing. In a developer's or application security engineer's professional life, the consequences of … [Read more...] about Secrets, Secrets Are No Fun. Secrets, Secrets (Stored in Plain Text Files) Hurt Someone
How to parse body text into Elastic App Search during data ingestion
Elastic App Search allows developers to bring the power of Elasticsearch to mobile apps in a pretuned search experience. When parsing body text, the App Search crawler extracts all the content from the specified website and spreads it in fields depending on the HTML tags it finds. Text within title tags are assumed as title field, anchor tags are parsed as links, and body is … [Read more...] about How to parse body text into Elastic App Search during data ingestion
How to deploy NLP: Text Embeddings and Vector Search
How to deploy NLP: Text Embeddings and Vector SearchEnglish简体中文한국어日本語FrançaisDeutschEspañolPortuguêsAs part of our natural language processing (NLP) blog series, we will walk through an example of using a text embedding model to generate vector representations of textual contents and demonstrating vector similarity search on generated vectors. We will deploy a publicly … [Read more...] about How to deploy NLP: Text Embeddings and Vector Search