Vector Databases in Practice: Deep Dive

Course details

Vector databases and their uses are transforming how we work. They are fundamentally changing how data is stored, managed, and retrieved through their deep integration with AI models. In this course, learn practical, end-to-end skills on how to build and use vector databases. Instructor Joon-Pil Hwang guides you through building an application that is primarily powered by a vector database, taking you all the way from database creation to usage and even app integration. Learn key considerations in using a vector database in practice, as well as be aware of some common techniques and baseline choices as starting points. Discover keyword, vector, and hybrid searches to find the right data faster, as well as how to apply retrieval-augmented generation - which makes generative AI tools more accurate by grounding them with your data.

Concepts

0. Introduction

01 - The power of AI-powered vector databases

1. Search Functions in a Vector Database

02 - A high-level view of vector databases
03 - What you can do with vector databases
04 - Get set up for the course
05 - Keyword filtering and keyword searches
06 - Vector searches
07 - Searching with filters
08 - Hybrid searches
09 - Retrieval augmented generation
10 - Challenge - Vector database queries
11 - Solution - Vector database queries

2. Building a Vector Database

12 - Create your own database
13 - Work with Weaviate
14 - Create an object collection
15 - Basic data import in Weaviate
16 - Establishing relationships with references
17 - Recap - Building a vector database
18 - Challenge - Add another object collection
19 - Solution - Add another object collection

3. Building a Vector Database-Powered App

20 - Web apps and vector databases
21 - Create a basic app
22 - Connect the app to Weaviate
23 - Parsing query responses
24 - Recommendations with RAG
25 - Challenge - App enhancements
26 - Solution - App enhancements

4. Making a Vector Database Work for Your Data

27 - Messiness of real data
28 - Pre-processing text for vector databases
29 - Chunking longer texts
30 - Chunk Wikipedia articles
31 - Challenge - Import Wikipedia data chunks
32 - Solution - Import Wikipedia data chunks

Conclusion

33 - Continue learning about vector databases