Advanced RAG 01: Small-to-Big Retrieval with LlamaIndex

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@sivi3883 Жыл бұрын

Thanks for sharing! Cannot wait to try this and see the difference in the quality of RAG output. Excited to hear more tricks in this series!!!

@danieltost Жыл бұрын

Loved the small to big retrieval. Simple and elegant. Awesome work Sophia!!

@whlau6191 Жыл бұрын

Sophia, thanks for your sharing! Your presentation is always clear, structural and target oriented. it is so nice that you are one of the speakers in HK pycon. See you! 😊

@chukypedro818 Жыл бұрын

Awesome, I've tried your approach to RAG, and it is far better than the current Langchain RAG that I am using. You are the best, Sophia.😘

@foreignconta Жыл бұрын

Actually I do not use llama_index. I have created a separate class which does cosine similarity search for my local LLM's internet search feature.. This small chunks big chunks method looks very interesting. I will watch this video again tomorrow. Very good information!! Thank you.

@SophiaYangDS Жыл бұрын

Nice! Thanks! Yeah the small to big retrieval methods are useful 🙌

@assouliyoussef9912 6 ай бұрын

just brilliant , thank you

@TheCherryo Жыл бұрын

I thought of something similar to this but never had the time to implement it myself. I am very excited to see all the advancements in RAG and Agent-like applications. Curious to see if there is going to be specialized models that work better with RAG applications.

@ztq121121 Жыл бұрын

Thank you for sharing this! I'm curious if there are any benchmarks or evaluations that quantify the extent of improvement that small-to-big retrieval can offer. Is there any research paper that we can use as reference?

@olliegooding 6 ай бұрын

great explanations!

@eklok5000 Жыл бұрын

Really interesting content! I am unfortunately not all that familiar with llms and those techniques so it is sometimes hard to follow. Do you have some resources to catch up on the fundamentals?

@JamesRBentley Жыл бұрын

Another great video👍

@MakeStuffWithAI Жыл бұрын

Awesome tutorial!

@crowofhermes 11 ай бұрын

Hi first of all, thank you for the great explanations! may I ask how does the first recursive retriever differs from utilizing `HierarchicalNodeParser + AutoMergingRetriever`? I will check in detail to see if there is something else but they seem pretty similar

@dhavalthakkar5147 Жыл бұрын

Sophia, is there any langchain implementation for the above techniques?

@klaudioz_ Жыл бұрын

Amazing content!!. Thanks for sharing 🚀

@SophiaYangDS Жыл бұрын

Thank you 🙏😊

@amitk3098 7 ай бұрын

Is there anyway I can implement this without langchain and llama-index? I am not in favor having my RAG solution dependent on them.

@sjsadjf7030 7 ай бұрын

Amazing video but I have a question. I would be very happy if you could respond; even though it enhances the accuracy a lot, recursive retriever takes so much time. Would persisting indexes to the disk also be a solution in this case? or is there an alternative way to reduce the time? Thank you very much!!

@sjsadjf7030 7 ай бұрын

oh, when I switched the embed model to default (ada-002-v2) it is now much faster, now I understand that it is not recursion related lol :D the main question has changed to how to reduce the embedding model costs haha

@avotchiezoa6627 Жыл бұрын

where can i find your background video ? Which apps do you use for power point?

@liju7024 Жыл бұрын

I still don't understand the connection between child chunck and parent chunck. Can you explain more about it?

@Aldraz Жыл бұрын

There are times when some new AI technique comes up and I am like yay.. but then I realize it's something I had implemented a year ago xD

@oguzhanzobar1094 Жыл бұрын

looks pretty cool, but I keep getting a ValueError: "Got a larger chunk overlap (200) than chunk size (128), should be smaller." when I try to run the code under "Chunk References: Smaller Child Chunks Referring to Bigger Parent Chunk".

@nathanredin7785 11 ай бұрын

add ",chunk_overlap=20" in your splitter