Self-Corrective RAG with LangGraph - Agentic RAG Tutorial

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Self-Corrective RAG with LangGraph - Agentic RAG Tutorial

Рет қаралды 1,844

Күн бұрын

In this video you will learn how to perform "CRAG", self-corrective Retrieval Augmentation with LangGraph.
You will learn how you can force an Agent to rewrite queries and check whether a document is suited to answer a question or not.
Code: github.com/Cod...
Timestamps:
0:00 Introduction to CRAG
1:00 Code Walkthrough
12:06 Results
#langchain #langgraph

Пікірлер: 9

@yosrivastava7982 Ай бұрын

These videos are really very helpful , one request please put all the recently added videos into a proper playlist for quick references like the one you did for langchain projects and other playlists..

@codingcrashcourses8533 Ай бұрын

I will do that

@Reality_Check_1984 Ай бұрын

I think you make excellent videos. My only request for future content would be for you to make a quick recap on your agent related projects to point out the changes that have to be made to make them work locally without OpenAI. Thank you for all of the content you produce. I have learned a lot from you.

@codingcrashcourses8533 Ай бұрын

Well that´s actually the beauty of langchain. Just replace the OpenAI classes with classes you like. They all share the same Runnable Interface. So instead of using ChatOpenAI, you can just use OLlama. from langchain_community.llms import Ollama Pretty easy with Langchain to switch models :)

@Reality_Check_1984 Ай бұрын

@@codingcrashcourses8533 I do that now with my applications and you are right it works extremely well. I am still navigating through ollamafunctions and what I think might be some payload adjustments I need to make on my end in some of the applications I have in development. Thank you again for consistently putting out great content.

@maxlgemeinderat9202 Ай бұрын

Cool, because of your videos I also switched to Langgraph and it is so much nicer in my opinion. However when i tested CRAG I realized that especially the document grade takes very long, which is not suitable for a production environment. What would you do to make this faster? Could also be slow for me because i am using mixtral 8x7b from Groq and not an OpenAi model

@codingcrashcourses8533 Ай бұрын

You can also try to classify multiple documents at once and ask the llm to write the index ([0:2] for example to then filter the correct docs. Less accurate probably, but faster