Semantic Search With Elasticsearch
This talk will present Elasticsearch as a capable vector database. It will explain the conceptual and technical differences between semantic and lexical search as well as the concept of embeddings and semantic similarity. Using data from Czech Wikipedia, the talk will focus on code-based demonstration of generating embeddings, indexing data and performing searches, with a particular focus on performance, as well as evaluating the quality of search results. Timestamps: 00:00 Introduction 02:00 What is Semantic Search? 05:43 What are Text Embeddings? 09:50 What is Semantic Similarity? 11:52 Generating Embeddings 17:05 How to Work with Vectors in Elasticsearch 19:26 Step 1: Pre-Process the Data 28:35 Step 2: Generate Embeddings 32:54 Step 3: Index Embeddings 40:00 Step 4: Search the Data 52:03 Step 5: Evaluate Search Quality Speaker: Karel Minařík Additional resource: https://github.com/karmi/talks Make sure to join your local Elastic User Group to stay up-to-date on upcoming meetups: https://community.elastic.co/ Questions? Check out https://discuss.elastic.co/ Connect with the Elastic community through Slack: https://ela.st/slack #semanticsearch #elasticsearch #vectordatabase #lexicalsearch #search #semantic #embeddings #techcommunity #techtalk
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