Share Description
In this course, you will learn:
- What is an embedding?
- Setting up a vector database
- Supabase & pgvector
- Semantic search
- Similarity search
- Chunking text documents
- RAG
Syllabus:
- What are embeddings?
- Set up environment variables
- Create an embedding
- Challenge: Pair text with embedding
- Vector databases
- Set up your vector database with Supabase
- Store vector embeddings
- Semantic search
- Query embeddings using similarity search
- Create a conversational response using OpenAI
- Chunking text from documents
- Challenge: Split text, get vectors, insert into Supabase
- Error handling
- Query database and manage multiple matches
- AI chatbot proof of concept
- Retrieval-augmented generation (RAG)
- Solo Project: PopChoice
