Vector Database in RAG Explained | ChromaDB, Embeddings, Similarity Search & HNSW

In today's RAG class, we move from *Embeddings → Vector Database → Similarity Search* using **ChromaDB**. After chunking a document and converting each chunk into an embedding vector, we need a way to store those vectors and efficiently find the most relevant chunks. In this class, we learn: 🔹 What is a Vector Database? 🔹 Why do we need a Vector Database after embeddings? 🔹 How ChromaDB stores vectors, documents, IDs and metadata 🔹 Vector Database vs traditional SQL Database 🔹 How similarity search works 🔹 Query embeddings and Top-K retrieval 🔹 Exact Search vs Approximate Nearest Neighbour (ANN) 🔹 HNSW and why it is important in ChromaDB 🔹 Cosine similarity / distance 🔹 Metadata filtering 🔹 How to create a ChromaDB collection 🔹 How to add documents and embeddings 🔹 How to search for relevant chunks 🔹 How Vector Database fits into the complete RAG pipeline RAG Pipeline *Document → Chunking → Embedding → ChromaDB → Similarity Search → Relevant Chunks → LLM → Answer* The practical examples in this class use *Python + ChromaDB* and are designed for students who are learning RAG from the fundamentals. #RAG #GenerativeAI #ChromaDB #VectorDatabase #Embeddings #SemanticSearch #HNSW #Python #LLM