Elasticsearch Relevance Engine (ESRE) Engineer
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Develop the skills and knowledge required to build RAG applications using natural language and deliver highly relevant results, generate contextually rich content, and solve real-world problems.
Course summary
In this course, students will learn how to leverage the Elasticsearch Relevance Engine (ESRE) and large language models (LLMs) to build advanced RAG (Retrieval-Augmented Generation) applications that combine the storage, processing, and search features of Elasticsearch with the generative power of an LLM.
Through lessons and hands-on labs, students will develop the skills and knowledge required to build RAG applications using natural language and deliver highly relevant results, generate contextually rich content, and solve real-world problems.
- Topics
- Audience
- Duration
- Pre-reqs
- Requirements
Topics
- ESRE Overview
- Retrieving Data from Elasticsearch
- Application Integration with Elasticsearch
- Full-Text Search
- Semantic Search
- Developing a GenAI Application
- Developing a RAG Application
- Elastic AI Assistant
ELASTICSEARCHRELEVANCEENGINE(ESRE)
ENGINEER
COURSE INFORMATION
Inthiscourse,studentswilllearnhowtoleveragetheElasticsearchRelevanceEngine(ESRE)andlarge language models (LLMs) to build advanced RAG (Retrieval-Augmented Generation) applications that combine the storage, processing, and search features of Elasticsearch with the generative power ofan LLM.
Through lessons and hands-on labs, students will develop the skills and knowledge required to build RAGapplicationsusingnaturallanguageanddeliverhighlyrelevantresults,generatecontextuallyrich content, and solve real-world problems.
Audience Developers SoftwareEngineers SolutionArchitects
Duration: 24Hours
LESSONS
ESREEngineeroverview
Provides an overview of RAG-based solutions usingESREtoobtainhigh-relevancecontentand create a context for use with ChatGPT.
Retrieving data from Elasticsearch DemonstrateshowQueryDSL,Compound Queries,andAggregationscanbeemployedto retrievedatafromElasticsearchtocreatethe appropriate context from private content.
ApplicationintegrationwithElasticsearch Details the variety of tools available for integrating RAG-based applications with Elasticsearch.
Semanticsearch
Demonstrates a variety of ways in which Elasticsearchsupportssemanticsearchusing Vector Search and ELSER.
DevelopingaGenAIapplication
Explores the foundational pieces of building a RAG-based application using LangChain and Streamlit.
DevelopingRAGapplicationswith Elasticsearch
Completes the RAG-based application development experience by building stateful contextsusingprivatedomain-specificcontent.
Language: English
Prerequisites
- In this course, the student will develop applications using the Python programming language.
- Astrongfoundationinsoftware developmentandprogramming concepts is required.
- Successful completion of the ElasticsearchEngineercourseisalso strongly recommended.
Requirements
- Stableinternetconnection
- Mac,Linux,orWindows
- LatestversionofChromeorFirefox (other browsers not supported)
- Disableanyadblockersandrestart your browser before class
ELASTICSEARCHRELEVANCEENGINE(ESRE)ENGINEER
LESSONS
Full-textsearch
Providesanoverviewofhowtextisanalyzed,stored,and retrieved in Elasticsearch.
GenAIat Elastic
Summarizesseveralsolutionsthatcanbebuiltusing ChatGPT and ESRE.
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