Vector search overview

AlloyDB AI enhances the open-source pgvector extension, providing full compatibility with its standard index types such as HNSW while supercharging performance with Google's cutting-edge ScaNN vector search technology. This unique integration lets you build scalable, production-ready generative AI, semantic search, and recommendation engines without moving your data or managing a separate vector database.

Build faster, smarter AI applications

AlloyDB AI eliminates the complexity of managing a separate vector database. Keep operational data and embeddings in one place, and use built-in functions to generate batch embeddings (Preview) for large tables, scaling your AI pipeline from a single, secure source.

Retrieval-augmented generation (RAG)

Enhance LLM responses by using vector search to find and retrieve the most relevant, up-to-date context from your private data in AlloyDB, directly feeding it into the prompt to reduce hallucinations.

Semantic search

Enable concept-based search by converting your data into vector embeddings. AlloyDB AI's fast vector queries then find items based on their semantic meaning, not just keyword matches.

Recommendation engines

Power real-time personalization by using ultra-fast vector search to identify similar items or users (nearest neighbors) directly within your database, enabling dynamic and relevant recommendations.