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

[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. 

Indexing and Retrieving Documents with LangChain

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

Indexing and Retrieving Documents with LangChain