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Fundamentals of Retrieval-Augmented Generation with LangChain
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1.
Getting Started
Why Choose RAG and This Course?
2.
The Basics of RAG
Understanding Retrieval and Generative Models
What Is Retrieval-Augmented Generation (RAG)?
Indexing Essentials: How RAG Organizes Data?
Effective Retrieval Strategies for RAG Pipelines
Quiz: Test Your Knowledge on RAG Basics
3.
RAGs and LangChain
Indexing and Retrieving Documents with LangChain
Creating Augmented Queries Using LangChain
Generating Context-Driven Responses with LangChain
Quiz: Reinforce Your LangChain and RAG Skills
4.
Build a Frontend for Our RAG System
What Is Streamlit?—An Intro for Beginners
Building a QA System Interface in Streamlit
Adding LangChain Logic to Your Streamlit Application
Quiz: Test Your Frontend and Streamlit Knowledge
5.
Challenges
Challenge: Transitioning Between Vector Stores
Solution: Transitioning Between Vector Stores
Challenge: Supporting PDF File Uploads in RAG
Solution: Supporting PDF File Uploads in RAG
Challenge: Building a QA System for Multiple Files
Solution: Building a QA System for Multiple Files
Project
Premium
Building a Retrieval-Augmented Generation System Using FastAPI
6.
Conclusion
Wrap Up
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Fundamentals of Retrieval-Augmented Generation with LangChain
Quiz: Test Your Knowledge on RAG Basics
Test your understanding of the basics of RAG.
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