Nyala Labs Bonfire - Vector databases: Building Persistent AI Memory

​This 1-hour technical deep-dive focuses on the mechanics of semantic retrieval. You will write Python code to transform raw text into high-dimensional embeddings, upsert them into a vector database, and execute k-nearest neighbor searches to retrieve contextually relevant data. Participants will walk away with a functional, searchable memory layer - the foundational storage engine required for any RAG-based application or production-ready AI agent.