Advanced Retrieval Methods for RAG

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In this event, we will break down the retrieval algorithms that AI Engineering practitioners should know and have at hand within their toolbox. Algorithms known to provide greater precision and results at the retrieval step of RAG include the Parent Document Retriever, Self Query Retriever, Contextual Compression, and Time-Weighted Vector Store.

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Speakers:
Dr. Greg, Co-Founder & CEO

The Wiz, Co-Founder & CTO

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On a scale from 1 to John Wick how much do you love your dog? 😆🤣🤣😆

leonardjin

Amazing content, exactly what I've been looking for to utilise in my pet project :D analysing some serious law documents...

bparlan

Great video and demo as always! You mentioned a RAGAS notebook that compares these techniques. Would be interested in seeing that too, if it’s convenient to add a link. (A demo/tutorial that deep dives into adding and utilizing metadata within the LlamaIndex environment would be super helpful.)

seanbergman

Hey guys, question for you:

I want to take a (1) source PDF document, (2) comprehends its content (meaning), and then (3) search through a database of documents with similar meanings/context to (4) find and display excerpts describing similar situations and decisions.

Example:
Take the legal case of FTX Sam Bankman Fried Crypto Scam, understands the key details of this case, and then do searches through a database of other legal cases to find and present excerpts that describe similar situations and decisions made in those cases.

Any suggestion on approach?
(I just started working on this on Sunday at a Law Hackathon that Stanford held; having problems with approach)

AmanBansil

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