4.5
Beginner
4h
Updated this week
Fundamentals of Retrieval-Augmented Generation with LangChain
Explore this beginner RAG course to learn the basics of retrieval-augmented generation. For hands-on practice, build RAG pipelines using LangChain and create user-friendly applications with Streamlit.
Retrieval-augmented generation (RAG) is a powerful paradigm that combines the strengths of information retrieval and generative AI models to produce accurate, context-relevant results. This method improves the efficiency of generative models by integrating external knowledge sources for various applications.
This beginner RAG course introduces learners to the fundamental concepts of RAG, offering a comprehensive understanding of its architecture and applications. You’ll learn how to implement RAG pipelines using LangChain, gaining hands-on experience building your first RAG solution. Additionally, you’ll create a complete frontend application using Streamlit, simplifying user interaction with your project.
After completing this course, you’ll have the skills to apply RAG principles and techniques to build practical RAG solutions using LangChain and Streamlit, setting a strong foundation for more advanced concepts.
Retrieval-augmented generation (RAG) is a powerful paradigm that combines the strengths of information retrieval and generative ...Show More
WHAT YOU'LL LEARN
A clear understanding of the basics of retrieval-augmented generation (RAG)
Practical experience implementing RAG pipelines using LangChain
The ability to build a frontend application for your RAG pipeline using Streamlit
Real-world application of RAG concepts to solve practical problems
A clear understanding of the basics of retrieval-augmented generation (RAG)
Show more
TAKEAWAY SKILLS
Content
2.
The Basics of RAG
5 Lessons
Learn the logic behind RAG, its essential components, and strategies like indexing and retrieval to build a solid foundation for your RAG systems.
3.
RAGs and LangChain
4 Lessons
Explore implementing indexing, querying, and response generation in LangChain to power your RAG systems.
4.
Build a Frontend for Our RAG System
4 Lessons
Use Streamlit and LangChain to build a user-friendly frontend for your RAG system, enabling seamless interaction with your pipeline.
5.
Challenges
6 Lessons
Tackle advanced challenges to enhance your system, like handling vector store transitions and supporting multiple file formats.
Certificate of Completion
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