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

[LangChain](https://www.educative.io/answers/introduction-to-langchain) is like the ultimate toolkit for language applications, especially when we speak about RAG systems. When designing a RAG system, we need different components that work with smooth interfacing, e.g., a retrieval component to locate the information and a generation component to build a response using the found information. LangChain not only helps us develop the construction of these components but also the interfacing among them.

Learn how LangChain supports effective data indexing and retrieval. 

Getting Started

The Basics of RAG

RAGs and LangChain

Build a Frontend for Our RAG System

Challenges

Conclusion

How to Index and Retrieve Documents in LangChain for RAG Systems