OpenAI_API

Gemini_API

This course covers RAG basics, architecture, and applications and teaches you to build RAG pipelines using LangChain and Streamlit.

Retrieval Augmented Generation (RAG) (Beginners).png

final.tar.gz

Base Python

Delete OAI

Chroma

Retrieval-augmented generation (RAG) is a robust paradigm that makes the most of the best information retrieval and generative model strengths to yield correct and context-relevant results. RAG enhances generative models by integrating external knowledge sources, making them more efficient in various use cases. 

This course introduces the learners to the basic concepts of RAG, giving them a comprehensive understanding of RAG architecture and applications. You’ll implement RAG using LangChain, gaining practical experience building RAG pipelines. You’ll also create a complete frontend application for your RAG pipeline using Streamlit, providing a simple user interface for your project. 

By the end of this course, you’ll be able to apply RAG principles and techniques to implement RAG solutions using LangChain and Streamlit.

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. 

Getting Started

The Basics of RAG

RAGs and LangChain

Build a Frontend for Our RAG System

Challenges

Conclusion

Challenge: Adding Support for PDF Files