OpenAI_API

Gemini_API

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) (Beginners).png

final.tar.gz

Base Python

Delete OAI

Chroma

OpenAI 1.5

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.

Fundamentals of Retrieval-Augmented Generation with LangChain

Up to this point, our RAG system has been handling only plain text (TXT) files or hardcoded strings. In this challenge, we're stepping up our game by introducing the capability to handle PDF files. This challenge will test your ability to adapt and enhance the functionality of our RAG system. Are you ready to take on the challenge?

Test your understanding of the RAG pipeline by customizing it to support PDF format. 

Challenge: Supporting PDF File Uploads in RAG

Test your understanding of the RAG pipeline by customizing it to support PDF format.

Getting Started

The Basics of RAG

RAGs and LangChain

Build a Frontend for Our RAG System

Challenges

Build an Interactive PDF Reader using LangChain and Streamlit

Conclusion

Challenge: Supporting PDF File Uploads in RAG

What to do?