How RAG Finds Answers in Millions of Documents - Embeddings, Vector Databases, LangChain and Supabase

Learn to build a scalable semantic search system that transforms text documents into AI-searchable knowledge bases using embeddings, vector databases, and RAG (Retrieval-Augmented Generation) applications. Discover what embeddings are and how they enable machines to understand text meaning through a practical toy example demonstration. Master the implementation of pre-trained embedding models using LangChain and understand the criteria for selecting the most appropriate embedding model for your specific use case. Explore the debate around vector databases and their necessity, then get hands-on experience with Supabase installation, setup, and integration with LangChain for vector storage and retrieval. Practice advanced techniques including metadata filtering to enhance search precision and learn how to scale your solution to handle millions of documents efficiently.