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

You've been making fantastic strides in mastering the RAG systems, and now it's time to broaden your horizons even further. Up to this point, we've been using Chroma as our trusty vector store, helping us efficiently manage and retrieve our document embeddings. However, the world of technology is vast and filled with diverse tools and methodologies, each with its unique strengths and use cases.

Test your understanding of the RAG pipeline by customizing it to use Qdrant vector store instead of Chroma.

Getting Started

The Basics of RAG

RAGs and LangChain

Build a Frontend for Our RAG System

Challenges

Conclusion

Challenge: Changing the Vector Store