Foundations of Vector Database

A vector database stores high-dimensional vectors generated from text, images, or other data. It enables fast similarity search, which is crucial for applications like semantic search, recommendations, and Retrieval-Augmented Generation (RAG) in modern AI systems.

Traditional databases store structured rows and columns, ideal for exact matches and filters. A vector database stores embeddings and focuses on similarity search, helping AI systems find “meaningfully similar” items rather than just exact keyword or value matches.

Embeddings are numerical representations of data, such as text or images, in a high-dimensional vector space. Vector databases store and index these embeddings, enabling efficient similarity search and powering use cases like semantic search and intelligent chatbots.

Similarity search lets you retrieve the most relevant documents or data points based on meaning, not just keywords. This is essential for RAG systems, where an LLM needs high-quality, contextually similar information to generate accurate, grounded responses.

FAISS and Chroma are popular tools for building vector databases and performing vector search. They handle indexing, storing, and querying embeddings efficiently, making them ideal for learning the foundations of vector databases and RAG systems.

Vector databases power semantic search, personalized recommendations, document search, chat-with-your-data apps, and Retrieval-Augmented Generation (RAG). Understanding what is a vector database and how it works helps you design smarter, context-aware AI solutions.