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.

ScaNN vector search performance

Up to 10x faster ScaNN index creation and filtered vector search than HNSW.

Uses 3x less memory than HNSW.

Up to 4x faster vector search queries than HNSW.

Up to 10x faster filtered vector search than HNSW.

Learn more

Explore our developer resources to build AI applications with AlloyDB's vector search capabilities.