Workshop Artifacts:
Google Drive: drive.google.c...
COlab Notebook: colab.research...
Agenda
Part 1 (20 min)
Introduction to Vector Search: Understanding the fundamentals and its application in recommendation systems.
Overview of OpenAI Embeddings: Exploring how OpenAI embeddings work
Rockset for Data Management: Demonstrating Rockset's features for handling large datasets efficiently.
Part 2 (40 min)
Building the Webapp: Guided session on developing a functional web application that utilizes Rockset's vector search capabilities and openAI's embedding model.
Here's the chapterization of the video with timestamps for KZfaq:
0:00 - Introduction and Workshop Overview
1:01 - Applications of Vector Search and AI
2:17 - Workshop Agenda Review
2:54 - Part 1: Understanding Vectors and Embeddings
3:35 - Explaining Data Vectorization
4:14 - What is a Vector Database?
5:37 - The Role of Vector Databases in AI
6:20 - Understanding High-Dimensional Data
7:00 - Example of Vector Use in Games
7:34 - Textual Data Vectorization
9:26 - Introduction to Vector Search
10:07 - Evolution of Vector-Based Data Representation
11:35 - Historical Context of AI and ML Techniques
12:57 - Utilizing OpenAI for Vectorization
13:38 - Workshop Part 2: Building the WebApp
14:15 - Architecture of the Recommendation System
15:43 - Addressing Audience Questions
16:47 - Accessing Workshop Resources
17:48 - Setting Up the Rockset Console
19:19 - Preparing the Data and Rockset Environment
20:19 - Importing Data into Rockset
23:30 - Registering for OpenAI and API Setup
25:08 - Finalizing OpenAI Registration
26:51 - Writing Query Lambdas in Rockset
28:26 - Addressing Live Questions
29:18 - Filtering and Metadata in Vector Search
30:41 - Defining Parameters in Rockset
32:38 - Running and Testing Queries
33:42 - Saving and Utilizing Query Lambdas
34:48 - Building the Web Application
36:10 - Designing the User Interface
37:37 - Flask Backend Integration
39:25 - Google Drive Setup for Application Files
40:29 - Installing Dependencies
41:19 - Copying Repository Content
42:46 - Configuring Flask and OpenAI in Collab
44:55 - Reviewing and Finalizing Code
46:19 - Flask Application Logic and Routing
47:24 - Demonstrating the WebApp Interface
48:12 - Processing User Inputs in Flask
49:30 - Final Review of Frontend and Backend Code
50:59 - Launching the Flask WebApp
52:07 - Setting Up Rockset API Keys
54:01 - Running the Flask Application
55:39 - End-to-End Application Demonstration
56:26 - Addressing Audience Feedback and Questions
1:00:07 - Closing Remarks and Future Workshops
1:02:54 - Open Q&A Session and Audience Engagement
1:03:39 - Conclusion and Session Wrap-Up