RAPTOR - Advanced RAG with LangChain

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval - new and powerful indexing and retrieval technique for LLMs. I will show you both theory and practice behind this.

Timestamps:
00:00 What is RAPTOR?
01:29 Create Documents (High & Low-Level)
11:36 Indexing
13:00 Retrieval

#RAPTOR #langchain

[andreypetrunin]

Спасибо! Наши знания становятся все шире и шире! )))

[eck]

Very interesting approach with getting optimal clusters size. I did something similar in the past, via genetic algorithms.

[kai]

Very impressive! Thanks for sharing!
It would be great to see a comparison of the results. Once with RAPTOR and once simply vectorizing the documents in the classic way.

[henkhbit]

Great video, better than the Langchain video. This concept has alot of overhead and thus a performance impacr 2 be practical. its a simimar approach with the master detail summarization but with extra steps i.e clustering etc.

[eck]

So you are filtering out any clustered points where probability of belongingness to a specific cluster is lower than specified threshold?
Haven't seen GMM algorithm before, looks very interesting.
Basically, you are filtering outliers by providing that threshold. Super cool.

[JeisonJimenez-tbnc]

Excellent video, I have a question and I want to ask questions to my documents (there are more than 3500), currently I am doing it dividing them in small chuck vectorizing them and storing them locally with chromadb and I am using the RetrievalQA class of langchain but I am not getting accurate results, but ambiguous answers, I am using the LLM Mistral-7b. How can I adapt this Raptor approach to my use case, is it possible to save this clouster classification in my vectors in chromadb?

[trashchenkov]

If we have completed all the steps to create a vector database using the RAPTOR method, and then the task of adding new documents appears, do we need to do everything all over again? Regularly updating the database can then become very expensive.

[MEvansMusic]

what is the purpose of the dimensionality reduction step prior to clustering? is it because clustering is computationally expensive and reducing before hand helps? or is there a different reason ?

[georgefraiha]

Great Video Love it. But does it work when we have thousands of periodically changing files use case or it will be very expensive

[rowdyjuneja]

Great video! Question - How does this process evolve in a production setting? What happens if I want to add new documents? It seems like you would need to rerun the entire process.

[YueNan]

Hey I got a issue, What if sum of cluster documents exceed maximum token of summary chain ?

[corpsed]

hello, i am trying to make a rag chatbot for academic books. what rag techniques do you suggest me to adopt. I have been a lot of videos lately but cannot decide on one method. is RAPTOR any good for my use case? I have minimum latency, have watched about fusion and crag also, but i they are too slow for a chatbot response.

[Girijeshcse]

I have a similar use case of having a long context as well as the output should also be large, like from the documents of all restaurants in a town. If I ask list down all the restaurants that serve Italian food, I tried RAPTOR but it's not working in this case, can you suggest what can be done here?

[mrchongnoi]

How would this work on a 259 page document like the Tesla 2020 Financial Report ?

[loicbaconnier]

Thanks very good video
Why don’t you try to make some clusterering on each on the 3 mains clusters ? Must be test no ?

[timtensor]

Was just wondering what your setup specs are. For me a simple langchain query takes around 4-5 mins

[saravanannatarajan]

Such a great topic and you covered very nicely, thanks!
Can we use the clustering method as mentioned in the video to create summarization task (since I feel its interesting) please suggest if you have any idea

[IljaUchiah]

in my street there is a restaurant called bella vista and the owner is giovanni. you live in osnabrück?