LlamaIndex Sessions: Practical Tips and Tricks for Productionizing RAG (feat. Sisil @ Jasper)

[@infraia]

Very helpful guys! Thanks !

[@seanbergman8927]

This was an excellent talk. Thanks so much for sharing your experience and this RAG framework. If there could be a follow up sometime with a sample notebook that uses these techniques, and a code walkthrough video , I’m sure many people would greatly benefit from it.

[@philipagenmonmen]

Super comprehensive. Thanks for this.

[@memsSudar]

Sisil's presentation was exemplary, addressing key pain points with innovation. Congratulations on your work!. We as a company also trying to solve all pain points you have mentioned in the pdf docs area. Thanks to Jerry for spotlighting talent like Sisil. Excited for more content!

[@bszhang2115]

a showcase sample notebook would be deeply appreciated!

[@jeevan999able]

this was fantastic, can you provide a tutorial notebook?

[@falven]

Would have loved if you shared the slides in the description. :)

[@karthikmukundakrishnan7853]

Is there a sample code you can post a link to, specifically for indexing the subdocs, then the chunks and retrieving them?

[@chenrannight2272]

amazing!!! Sisil really explained the difference between benchmarks and real world openended questions! btw, could you include the datasets name Sisil talked about regarding "retrieval" and "reranking" on evaluation?

[@jeevan999able]

how does lexical indexing work for subdocs

[@qingsongyao4974]

I wonder what is the PDF parse Sisil 's team is using?

[@bram1223]

Adobe Extract PDF API or something. I haven't tested it but it works pretty well from what I've heard.

[@UdayVakalapudi]

There is an opensource version as well (Works in Linux Distro) called unstructured. You just need to do pip install unstructured[all].

[@DanielHernandez-n1o]

I didn't catch what was the pdf parser used? Can you name it please?

[@jochenade]

I think it was adobe api