Vector Database Explained for AI Agents & RAG Systems

Welcome back to Bazai! 🚀 In this video, we break down what actually happens when an object is added into a vector database. Learn how modern AI systems use embeddings, vector indexes, inverted indexes, and semantic search to power RAG applications, AI agents, copilots, recommendation systems, and enterprise AI search. We cover: ✅ Object insertion workflow ✅ Embedding generation ✅ Vectorization process ✅ Vector indexes & HNSW ✅ Inverted indexing ✅ Semantic similarity search ✅ Hybrid search architecture ✅ AI retrieval pipelines ✅ Storage and indexing flow ✅ How modern vector databases work internally This video is perfect for developers, AI engineers, cloud architects, and anyone building next-generation AI applications. Technologies & Concepts Covered: Vector Databases Embeddings Semantic Search Hybrid Search RAG Systems AI Agents HNSW Indexing Metadata Filtering AI Memory Systems Generative AI Infrastructure Subscribe to Bazai for advanced AI engineering, cloud-native AI systems, autonomous agents, and modern developer workflows.