Fundamentals of Vector Databases

I started working with vector databases in 2023. I worked on Milvus during my time at Zilliz and was one of the early pioneers in building RAG applications. My RAG courses on LinkedIn have garnered tens of thousands of learners and my blogs have reached over 250,000 people.

Vector databases solve an incredibly important topic for the LLM era, semantic similarity. Semantic similarity is how similar words are to each other (also applies to video, audio, etc). This allows the system to perform similarity searches, finding data points that are "mathematically close" to one another even if they don't share specific keywords. As Large Language Models (LLMs) rose to prominence, vector databases became essential for Retrieval-Augmented Generation (RAG), acting as a long-term memory for AI by providing relevant context from massive datasets in milliseconds.

• Embedding Models

• Vector Similarity

• Vector Distance Metrics

• Dense vs Sparse Vectors

• Vector Indexing