Name: Learn Embeddings and Vector Databases
Price: -1 USD

Learn Embeddings and Vector Databases

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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