What is a vector database? Enhancing semantic search and AI applications

Summary

Vector databases enable semantic similarity searches that traditional relational databases cannot perform by storing unstructured data as high-dimensional embeddings.

Why it matters

They represent data in a way that aligns with human perception, closing the 'semantic gap'; this is crucial for AI applications like image retrieval and Retrieval-Augmented Generation (RAG). By employing approximate nearest neighbor indexing techniques, they make large-scale, high-dimensional searches computationally feasible.

Highlights

• Convert images, text, and audio into vector embeddings that capture their semantic essence.
• Perform fast, approximate similarity searches using ANN algorithms such as HNSW and IVF.
• Serve as the foundation for RAG systems that feed large language models with relevant retrieved pieces.

Critical take

Embeddings are often opaque, making it difficult to understand why two items are considered similar; additionally, the speed-accuracy trade-off in ANN indexing can sometimes miss truly relevant results.