Vector Search and Embeddings: AI-Powered Search
Learn to build advanced search applications using Vertex AI Vector Search and LLM embeddings. Explore vector search concepts and practical implementation.
Learn to build advanced search applications using Vertex AI Vector Search and LLM embeddings. Explore vector search concepts and practical implementation.
This course introduces Vertex AI Vector Search and its application in building search engines using large language model (LLM) APIs for embeddings. Students will learn about vector search processes, applications, and key technologies, as well as understand embeddings and LLM APIs used for creating them. The curriculum includes conceptual lessons on vector search and text embeddings, practical demonstrations of building vector search on Vertex AI, and a hands-on lab. This course is designed for intermediate-level learners with some related experience in AI and cloud technologies.
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What you'll learn
Understand the fundamentals of vector search and its applications in modern search engines
Learn about embeddings and how they are generated using large language model (LLM) APIs
Gain practical knowledge on building a search engine using Vertex AI Vector Search
Explore the process of creating and using embeddings in search applications
Understand the key technologies and components involved in vector search
Learn how to implement vector search in conjunction with Retrieval-Augmented Generation (RAG)
Skills you'll gain
This course includes:
39 Minutes PreRecorded video
1 quiz
Access on Mobile, Tablet, Desktop
FullTime access
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There is 1 module in this course
This course provides a comprehensive introduction to vector search and embeddings, focusing on their application in building advanced search engines using Google Cloud's Vertex AI Vector Search. The curriculum covers the fundamental concepts of vector search, including its processes, applications, and key technologies. Students will learn about embeddings and how large language model (LLM) APIs are used to generate them. The course combines theoretical knowledge with practical implementation, featuring demonstrations on building vector search applications on Vertex AI. A key component of the course is a hands-on lab that allows students to apply their learning in a real-world scenario. The course also touches on advanced concepts such as Retrieval-Augmented Generation (RAG), providing a well-rounded understanding of modern search technologies.
Vector Search and Embeddings
Module 1 · 2 Hours to complete
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