Hey there.
Ethan here.
Super glad you got so far.
And in this video I want to discuss what we will be building in this section.
And we'll be implementing an advanced complex Rag workflow that is going to yield us much more quality
results than we're used to in regular Rag systems, all by leveraging land graph.
So this project is inspired by the Long Chain and Mistral Cookbook, where they introduced this topic,
and their code is listed in the courses repository.
And they also have a very good YouTube video.
But what I felt lacking in their cookbook is the software engineering perspective.
So what I did is to take their code and change it and refactor it, so it would be more production oriented.
That is, it will be more maintainable, more readable, more testable, and it's easier to extend if
you want to add new functionality.
What I also did is also to gradually build the system from zero, and to show you the software engineering
perspective while building an advanced workflow.
So what is this advanced Rag workflow I keep talking about?
It's based on three papers research papers, Self Rag, Corrective Rag, and Adaptive Rag.
And we'll be covering the gist of those papers further in the videos, but the general idea of those
papers is to add reflection into our workflow.
So we want to reflect on the documents that we retrieve and to see if they are indeed correct for us
or not.
We want to curate those documents and add new information if it's not enough.
After our reflection, we also want to reflect on the answer.
So we want to check if the answer is indeed grounded in the documents, and that the answer indeed answers
the question.
So a lot of reflection is going to be added here.
And also there is a routing element where we're going to route our request to the correct data store
that is going to have the information for our answer.
So that's a very quick overview of what we're going to be implementing.
And all of the code for this section is in a public GitHub repository, which you can reference at any
time when you're watching the videos.
So I've arranged it so each video will match its corresponding branch.
And you can even find the code at the end of each video, which is going to match the code in the repository.
So feel free to check it out and I hope you'll enjoy this section.
And let's start with the first part of implementing the research paper of Corrective Rag.