If you want to learn:
- How does Retrieval-Augmented Generation (RAG) actually work with vector databases?
- What is the difference between traditional RAG and agentic RAG systems?
- How do you build a complete data ingestion pipeline for AI applications?
- What are vector embeddings and how do they enable semantic search?
- How do you chunk documents effectively for vector storage?
- What are the two distinct phases of building production-ready RAG systems?
Then this lecture is for you!
This lecture provides a comprehensive deep dive into building RAG systems using Supabase vector database and pgvector. You'll master the fundamentals of Retrieval-Augmented Generation, understanding how vector embeddings transform text into searchable numerical representations for semantic search and similarity search operations.
The lecture covers the complete RAG architecture, breaking down the two critical phases: data ingestion pipelines and query retrieval workflows. You'll learn the extract, transform, and load (ETL) process specifically adapted for vector stores, including document chunking strategies, embedding generation using OpenAI models, and vectorization techniques that enable efficient vector search across large datasets.
You'll explore the evolution from traditional RAG to agentic RAG, where AI agents autonomously manage retrieval workflows using multiple tools and iterative approaches. The lecture demonstrates how Supabase, an open-source Postgres database built on top of pgvector, provides scalable vector storage capable of handling millions of embeddings with millisecond query performance.
Key technical concepts include vector similarity search, metadata integration, schema design for documents tables, and the practical implementation of RAG pipelines using n8n for workflow automation. You'll understand how embedding models (encoders) convert user queries into vectors, how HNSW indexes optimize retrieval speed, and why chunking strategies must be tested against your specific dataset and use cases.
The lecture also addresses common misconceptions about RAG being obsolete due to larger context windows, explaining why vector-based retrieval remains essential for scalable AI applications and efficient resource utilization in production environments.