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UID:pretalx-2024-W3ZJWG@cfp.scipy.org
DTSTART;TZID=PST:20240709T080000
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DESCRIPTION:As we descend from the peak of the hype cycle around Large Lang
 uage Models (LLMs)\, chat-based document inquiry systems have emerged as a
  high-value practical use case. Retrieval-Augmented Generation (RAG) is a 
 technique to share relevant context and external information (retrieved fr
 om vector storage) to LLMs\, thus making them more powerful and accurate.\
 n\nIn this hands-on tutorial\, we’ll dive into RAG by creating a persona
 l chat app that accurately answers questions about your selected documents
 . We’ll use a new [OSS project called Ragna](https://ragna.chat/en/lates
 t/) that provides a friendly Python and REST API\, designed for this parti
 cular case. We’ll test the effectiveness of different LLMs and vector da
 tabases\, including an offline LLM (i.e.\, local LLM) running on GPUs on t
 he cloud-machines provided to you. We'll then develop a web application th
 at leverages the REST API\, built with [Panel](https://panel.holoviz.org)
 –a powerful OSS Python application development framework.
DTSTAMP:20260315T234132Z
LOCATION:Ballroom B/C
SUMMARY:From RAGs to riches: Build an AI document inquiry web-app - Pavithr
 a Eswaramoorthy\, Dharhas Pothina\, Andrew Huang
URL:https://cfp.scipy.org/2024/talk/W3ZJWG/
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