OpenAI_API

Gemini_API

Gain insights into retrieval-augmented generation by delving into RAG architecture. Learn to implement RAG using LangChain and build user-friendly applications with Streamlit for efficient data retrieval and generation.

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

Test your understanding of the basics of LangChain in the context of RAG systems. 

Quiz: RAG and LangChains

Test your understanding of the basics of LangChain in the context of RAG systems.

Getting Started

The Basics of RAG

RAGs and LangChain

Build a Frontend for Our RAG System

Challenges

Conclusion

Quiz: RAG and LangChains