Ready to boost your AI career by mastering next-level retrieval techniques for intelligent search and summarization? This hands-on course takes you deep into the world of Retrieval-Augmented Generation (RAG), advanced retrievers, and vector databases such as FAISS and Chroma DB. You'll gain the cutting-edge skills businesses need to design and build scalable, high-performance RAG applications that drive smarter search and response capabilities.
Advanced RAG with Vector Databases and Retrievers
Advanced RAG with Vector Databases and Retrievers
This course is part of multiple programs.
Instructors: Wojciech 'Victor' Fulmyk
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Gain insight into a topic and learn the fundamentals.
4.6
(30 reviews)
Intermediate level
Recommended experience
7 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Build RAG applications using vector databases and advanced retrieval patterns
Employ the core mechanics of Vector Databases such as FAISS and Chroma DB and implement indexing algorithms like HNSW
Implement advanced retrievers using LlamaIndex and LangChain to improve the quality of LLM responses
Develop comprehensive RAG applications by integrating LangChain, FAISS, and front-end user interfaces built using Gradio
Skills you'll gain
Details to know
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Assessments
5 assignments
Taught in English
Recently updated!
August 2025
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There are 2 modules in this course
In this module, you will get a deep dive into advanced retrievers and retrieval patterns, equipping you with the skills to implement and optimize advanced retrieval strategies within a RAG system. Participants will explore various retriever types through video lectures and hands-on labs, including vector store-backed, multi-query, self-querying, and parent document retrievers. Learners will apply these techniques using LangChain and LlamaIndex, gaining practical experience in building smarter search capabilities and enhancing retrieval efficiency in AI-driven applications.
What's included
5 videos2 readings3 assignments4 app items1 discussion prompt2 plugins
In this module, you will explore FAISS, a powerful vector database used for efficient similarity search. You will compare FAISS with Chroma DB to understand its unique advantages and applications. Through hands-on experience, you will build a semantic search engine using FAISS in a non-RAG setting, demonstrating its versatility beyond retrieval-augmented generation (RAG). Finally, you will develop a fully functional RAG application, integrating FAISS, an advanced retriever, and a front-end UI built with Gradio. This module reinforces key RAG concepts while guiding learners through the process of creating an end-to-end AI-powered application.
What's included
2 videos3 readings2 assignments3 app items2 plugins
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