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LinkedIn Learning

Vector Databases in Practice: Deep Dive

via LinkedIn Learning

Overview

Go beyond the basics of vector databases by building a database and app from scratch, and learn key considerations along the way.

Syllabus

Introduction
  • The power of AI-powered vector databases
1. Search Functions in a Vector Database
  • A high-level view of vector databases
  • What you can do with vector databases
  • Get set up for the course
  • Keyword filtering and keyword searches
  • Vector searches
  • Searching with filters
  • Hybrid searches
  • Retrieval augmented generation
  • Challenge: Vector database queries
  • Solution: Vector database queries
2. Building a Vector Database
  • Create your own database
  • Work with Weaviate
  • Create an object collection
  • Basic data import in Weaviate
  • Establishing relationships with references
  • Recap: Building a vector database
  • Challenge: Add another object collection
  • Solution: Add another object collection
3. Building a Vector Database-Powered App
  • Web apps and vector databases
  • Create a basic app
  • Connect the app to Weaviate
  • Parsing query responses
  • Recommendations with RAG
  • Challenge: App enhancements
  • Solution: App enhancements
4. Making a Vector Database Work for Your Data
  • Messiness of real data
  • Pre-processing text for vector databases
  • Chunking longer texts
  • Chunk Wikipedia articles
  • Challenge: Import Wikipedia data chunks
  • Solution: Import Wikipedia data chunks
Conclusion
  • Continue learning about vector databases

Taught by

Joon-Pil Hwang

Reviews

4.7 rating at LinkedIn Learning based on 31 ratings

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