Chunk large complex PDFs to summarize using LLM
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- Опубликовано: 29 сен 2023
- In this video, I talk about a technique to do context aware chunking of large PDFs and then summarize the content using map-reduce framework(implemented through Langchain)
References:
arxiv.org/pdf/2307.03172.pdf
developer.adobe.com/document-...
python.langchain.com/docs/use...
smith.langchain.com/hub/
code:github.com/rajib76/langchain_...
oh man.. thanks for your videos! They are precious gold! I love the way you think and teach!
Man, very great explaination. I m constantly visiting your channel for great new tutorials @@@:)
Excellent, nice idea and very well explained! Thanks!
nicely explained!
Thanks Sir. Actually I am working on some project based on it and find difficult for me to find materials to understand the concept practically. After watching this video, I understand and implement it successfully and step ahead.
At last Thanks Sir for this video.
you'll be famous soon
Maybe for image, how does GPT4 multimodal models work ?
Hi Rajib, Thanks for making this video. It has been really helpful as I try to build a RAG system for a B2B use case. However, I did try setting up the Adobe API but I must say it's not too easy as I am getting stuck at various steps. I am not able to get 201 response code. Can you please share the steps you followed to setup the API? Regards, Bilal
Hi , Could you please share your LinkedIn profile ? I am doing the same PoC , I need some clarification.
Hi Rajib,
Really insightful video. Especially the Extract API for the context-aware extraction of text from PDF.
Are you aware of any open-source alternatives for the Extract API?
Regards,
Dev
:) I was also looking for one. Tabula, Camelot did not work for me. Looks like the enterprise grade solutions come with a price.
Closest open source I found working is unstructuredIO
that also did not work for me, in fact i have shared the results with unstructured
@@MadhanAnbalagan-ff5qt
if you had to pick an open source alternative for this, what would you chose? @@rajibdeb4059
@@MadhanAnbalagan-ff5qt Did you find a okaish opens source one?
Thank you, can you give your LinkedIn handle