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Robot committed Oct 28, 2024
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24 changes: 24 additions & 0 deletions src/content/sessions/HBB3ST.yml
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title: How to secure, break, and re-secure an encrypted data vault using Python and
PostgreSQL
start: 2024-11-25 13:30:00+11:00
end: 2024-11-25 15:30:00+11:00
room: chancellor2
track:
abstract: "<p>You’ve got sensitive customer data you need to protect in your Python
web service.</p>\n<p>You’ve done all the right things to secure it — using SQLAlchemy
to sanitise inputs to your SQL queries, HTTPS with Let’s Encrypt, and Semgrep in
your CD pipeline.</p>\n<p>You’ve even gone the extra mile — using cryptography and
SQLAlchemy’s StringEncryptedType to encrypt each row of data with AES.</p>\n<p>But
you have a lingering feeling that it’s all not quite enough. So how do you shake
that feeling?</p>\n<p>One of the best ways to understand a system is to break it
and rebuild it. That is what we’re going to do in this session.</p>\n<p>In this
workshop you’ll learn:\n * How to secure, break, and then re-secure an encrypted
data vault built using Python and PostgreSQL\n * The cryptography fundamentals you
need to pay attention to, and the ones you don’t\n * Plus: how quantum safety should
affect how you encrypt data today</p>"
description: ''
code: HBB3ST
speakers:
- PB9BKD
- KWK79M
cw:
26 changes: 26 additions & 0 deletions src/content/sessions/PQAV73.yml
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title: 'Mastering RAG & Unlocking AI Potential: Build a RAG system using Python,
Open-source LLMs & MongoDB Atlas'
start: 2024-11-25 09:30:00+11:00
end: 2024-11-25 12:30:00+11:00
room: chancellor2
track:
abstract: "<p>In this workshop, we will build a ChatBot based on Retrieval Augmented
Generation (RAG). The ChatBot will leverage MongoDB Atlas, embedding models, Large
Language Models (LLMs) to generate contextualized answers and textual content in
accordance to users’ queries based on publicly available sources.</p>\n<p>In this
hands-on workshop, attendees will:\n * Learn the foundations of Retrieval Augmented
Generation (RAG), such as chunking, embedding, and semantic search\n * Perform semantic
search queries against a MongoDB Atlas collection \n * Build a simple RAG system
using MongoDB Atlas and open-source LLMs</p>\n<p>Add memory to the RAG application\n
* In addition to these goals, the lab also offers more advanced content that covers:\n
* Combining pre-filtering with vector search\n * Adding re-ranking to the RAG application\n
* Stream responses from the RAG application</p>\n<p>Attendees will be provided with
all the resources required to successfully execute the hands-on portions of the
workshop, including a GitHub repository consisting of notebook templates with pseudocode.
Attendees will replace the pseudocode with their own code during the workshop.</p>"
description: <p>This workshop will be facilitated by experts from MongoDB</p>
code: PQAV73
speakers:
- PB9BKD
- KWK79M
cw:

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