Google’s BigQuery has recently introduced a new feature that is set to revolutionize the way organizations leverage their data: Vector Search. This functionality, also known as approximate nearest-neighbor search, is key to empowering numerous new data and AI use cases such as semantic search, similarity detection, and retrieval-augmented generation (RAG) with a large language model (LLM).
What is Vector Search?
Vector search in BigQuery allows you to search embeddings to identify semantically similar entities. Embeddings are high-dimensional numerical vectors that represent a given entity, like a piece of text or an audio file. Machine learning (ML) models use embeddings to encode semantics about such entities to make it easier to reason about and compare them.
How Does Vector Search Work?To perform a vector search, you use the VECTOR_SEARCH function and optionally a vector index. When a vector index is used, VECTOR_SEARCH uses the Approximate Nearest Neighbor search technique to help improve vector search performance, with the trade-off of reducing recall and so returning more approximate results.
Why is Vector Search Important?The introduction of vector search in BigQuery is a significant step forward in data analytics. It simplifies combining vector search operations with other SQL primitives, enabling you to process all your data at BigQuery scale. It works with BigQuery’s embedding generation capabilities, notably via LLM-based or pre-trained models. Yet the generic interface allows you to use embeddings generated via other means as well.
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