What is a Vector Database? Powering Semantic Search & AI Applications

Searching unstructured data with traditional fields and tags often misses what people actually mean—especially when you need “similar” images, text, or audio, not exact matches. This video explains how vector databases address the “semantic gap” by storing data as vector embeddings and enabling similarity search.

Key takeaways

• Breaks down how embeddings represent the “semantic essence” of unstructured data, with examples using images (mountain vs. beach sunsets).
• Explains how embedding models create high-dimensional vectors and why individual dimensions aren’t usually interpretable.
• Walks through vector indexing for scale, including approximate nearest neighbor (ANN) approaches like HNSW and IVF.
• Covers how vector databases support retrieval-augmented generation (RAG) by retrieving relevant text chunks for an LLM.