This is a great course to understand vector DBs / RAG concepts and get practical working examples. Not giving 5 stars because Jupyter exercise are not working anymore: it gives warnings about deprecated library langchain, and after updating pip install calls, 2nd and 3rd notebooks needs adaptation in the code.
LLM Foundations: Vector Databases for Caching and Retrieval Augmented Generation (RAG)
With Kumaran Ponnambalam
Liked by 184 users
Duration: 1h 33m
Skill level: Advanced
Released: 2/23/2024
Course details
As large language models grow in popularity, the infrastructure to be used around them also becomes vital to reduce costs, generate accurate responses, and improve efficiency. Vector databases play a vital role in several LLM use cases to help alleviate LLM shortcomings, reduce costs and latency. Knowledge of its basics and applications are vital for any engineer building applications with LLMs, and in this course, Kumaran Ponnambalam teaches you the basics of vector databases and how to use them in LLM caching and retrieval-augmented generation (RAG).
Kumaran begins with a discussion on the basics of vector databases and their applications. He then explores specialized databases for storing vectors and uses the Milvus database as the reference example, and demonstrates read and write operations with the Milvus database. Learn how to use vector databases for LLM caching, with an example use case, along with examples of RAG use cases. Finally, Kumaran concludes with a discussion on optimizing vector databases.Skills you’ll gain
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Meet the instructor
Learner reviews
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Dong Seo
Dong Seo
Senior Principal Technical Support Engineer | SCM/ERP Expert | Oracle Specialist | Multilingual Software Engineer
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Contents
What’s included
- Practice while you learn 1 exercise file
- Test your knowledge 4 quizzes
- Learn on the go Access on tablet and phone
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