At this point, you are practically Captain Chunk.

Thank you! I’ve been doing this for a while, but did not have a good name for it.

Need a video on cross-chunk attention. Wasn’t attention all about key query and val anyway

best i have seen so far about understanding core concept of chunking , thanks

@jamesbriggs

Ай бұрын

glad it was helpful :)

Love all your content sir!

Another killer video. Great work!

Amazing video, thank you so much!!

Thank you so much for this. Will test it out on the RAG flow in the company.

@jamesbriggs

Ай бұрын

welcome, would love to hear how it goes

King of Chunk

@jamesbriggs

Ай бұрын

a title I have always wanted

Great material. 🙏

Great video. Thanks for posting. I have been thinking of document chunking but using the LLM itself via prompting + k-shot. The approach you show will be cheaper of course but curious to see how these two approaches will compare in terms of any relevant non-cost metrics.

Excellent content and explanation , espeicialy chunking core concepts and challenges. Keep going your work it's so precisous to learn 👍

@jamesbriggs

Ай бұрын

Glad to hear it helps

Just what i eas trying to lewrn ...awesome mate, thanks

@jamesbriggs

Ай бұрын

Nice np

"Grab complete thoughts" is an obvious good and expensive thing. Except for tables, for instance.

@jamesbriggs

29 күн бұрын

yeah tables need to handled differently - doable if you are identifying text vs. table elements in your processing pipeline

What's a good way to use the metadata for retrieval and ranking of the chunks?

Can you introduce some articles related to this topic? Thanks!

Hi James , would you please tell me how you would tackle this one.. How would you design a realtime updating rag system? For example, let's say our clients updated some details in some watched doc, I want the old chunks to be removed, and rechunked automatically. Have you seen such pipeline existing already? No one seems to cover this and I think it sets apart fun projects and actual production system. Thanks and all the best! Love your channel ❤

@shameekm2146

Ай бұрын

I have achieved this for one of the sources in my RAG bot. It has an api provided to access the data. So i run the embedding script on the delta changes.

@AGI-Bingo

Ай бұрын

@@shameekm2146 amazing, would you please opensource it so we can all improve the pipeline as a community? 🌈

Can this be used to create chunks for creating a training dataset as well? It would be great to chunk a document into 'statements' and use those statements for a dataset. In essence have a LLM create questions for each of those statements and use those pairs for training. Could you make a video to show how that works?

How does Parent Document Rag fits in your in your new techniques?

Hi James. Excuse me, maybe I missed it. But how you handle the situation that when we use semantic chunking we miss pages numbers for chunks? Is it possible to receive it with using this package?

I see the #abstract is also with #title ideally both should be in different chunks so that LLM can understand better semantics.

Dude already embedded whole documents of texts into PC haha would've helped a month ago. But awesome thanks for this! 🤘🏾

@jamesbriggs

Ай бұрын

Maybe for the next project 😅

@talesfromthetrailz

Ай бұрын

@@jamesbriggs quick question man. Is the objective of semantic chunking to achieve broader search results? Or to decrease query times? I'm thinking of it in terms of medium sized text docs, for example movies summaries and such. Thanks!

Do you think this causes the results to be biased towards smaller chunks? Because the user will only query probably no more than 10 words . So the highest semantic similar results may also only be 10 words and the chunks that are 400 tokens wouldn't have as high as a score unless you provide more context to the query?

and how can I use big models from huggingface ? I can't load them into memory because many of them are bigger than 15gb, some of them are 130gb+ . Any thoughts?

We use a simple combination of Microsoft's Document Intelligence with markdown output and a simple markdown splitter. The improvement is noticeable although the Document Intelligence models do come at an additional cost.

@jamesbriggs

Ай бұрын

yeah it depends on what you need ofcourse, I'm mostly interested in further abstraction and more analytics methods for chunking not for where it is now, but for where this type of experimentation might lead to in the future - I could see a few more iterations and improvements to more intelligent doc parsing and chunking to become increasingly more performant - but we'll see

@alivecoding4995

Ай бұрын

Do you have a link for this markdown processing? :) We are using Document Intelligence as well, but not for layout analysis, yet.

@user-os6uo8xq9g

29 күн бұрын

@@alivecoding4995you can also use layoutpdf reader from llmsherpra

using a high end LLM like GPT-4 or Opus or Gemini Ultra or Pro might be effective in performing semantic chunking.. Google large context window seems suitable for chunking large files.. we need to introduce LLM in automating the RAG stack

@jamesbriggs

14 күн бұрын

Yeah I’d like to introduce an LLM chunker and see how they compare

@bastabey2652

14 күн бұрын

@@jamesbriggs better than any non LLM chunker.. if we aim to empower user's with AI, why not empower the developer? chunking is not easy

People since GPT2: Simply ask an LLM recursively to please insert “{split}“ where a topic change etc happens according to a summary of prior text. Get embeddings. Use to separate and group. 2024: We would like to introduce a novel concept called Semantic Chunking with a sliding Context…….. Beginners must be truly lost 😮‍💨

At ~4:40, you mention that you should use the same encoder for the chunking and the encoding. Why? A chunk size captures a "single meaning", so why would it matter that the same encoder is used? If you look at the chunking as a clutering algorithim that creates meaningful chunks, then what does it matter that the encoders match? What am I missing?

@jamesbriggs

Ай бұрын

good point - yes they are capturing the "single meaning" and that single meaning will (hopefully) overlap a lot, but embedding models are not perfect and so they will not align between themselves. Similar to if someone asked myself and you to chunk an article, we'd likely overlap for the majority of the article, but I'm sure there would be differences

Why not chunk based on paragraphs, lists, and tables.

My son just asked if you were the Rock

@jamesbriggs

27 күн бұрын

I hope you said yes

"What is the title of the document?" -> 99% of RAG pipelines fail, because there is not answer in the document as it is embedded,

@jamesbriggs

Ай бұрын

in that case we can try including the title in our chunk, and possibly consider different routing logic for this type of query - something that triggers when a user asks for metadata about a received document we trigger a function that identifies the document ID in previously retrieved contexts, and uses that to pull in the document metadata for the answer to be generated by the LLM

I am facing the problem in my jupyter notebook as this, please help 2024-05-10 10:59:50 WARNING semantic_router.utils.logger Retrying in 2 seconds...