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Scrimba

Learn Embeddings and Vector Databases

via Scrimba

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Overview

Learn how to improve the accuracy and reliability of LLM-based apps by implementing Retrieval-augmented Generation (RAG) using embeddings and a vector database.
  • What is an embedding?
  • Setting up a vector database
  • Supabase & pgvector
  • Semantic search
  • Similarity search
  • Chunking text documents
  • RAG

Syllabus

  • Learn Embeddings and Vector Databases
    • 1. Your next big step in AI engineering
    • 2. What are embeddings?
    • 3. Set up environment variables
    • 4. Create an embedding
    • 5. Challenge: Pair text with embedding
    • 6. Vector databases
    • 7. Set up your vector database with Supabase
    • 8. Store vector embeddings
    • 9. Semantic search
    • 10. Query embeddings using similarity search
    • 11. Create a conversational response using OpenAI
    • 12. Chunking text from documents
    • 13. Challenge: Split text, get vectors, insert into Supabase
    • 14. Error handling
    • 15. Query database and manage multiple matches
    • 16. AI chatbot proof of concept
    • 17. Retrieval-augmented generation (RAG)
    • 18. Solo Project: PopChoice
    • 19. You made it to the finish line!

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