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A deep dive into AlloyDB’s vector search enhancements
- AlloyDB for PostgreSQL offers vector search capabilities for structured and unstructured data queries, essential for generative AI applications and AI agents.
- AlloyDB AI’s ScaNN index enhancements at Google Cloud Next 2025 aim to boost search quality and performance across both structured and unstructured data.
- Filtered vector search in AlloyDB enables efficient search across metadata, texts, and images in scenarios like managing a vast product catalog for an online retailer.
- AlloyDB's query planner optimizes filter selectivity to determine the order of SQL filters in vector search, improving search speed and precision.
- Highly selective filters, like rare colors in a product catalog, are usually applied as pre-filters to reduce the candidate pool for vector search.
- Low-selectivity filters, such as common product attributes, are more efficiently applied as post-filters after an initial vector search to avoid excessive candidates.
- For medium-selectivity filters, AlloyDB supports in-filtering, applying filter conditions alongside vector search to strike a balance between pre-filtering and post-filtering.
- AlloyDB's adaptive filtration feature allows dynamic adjustment of filter order based on observed statistics, enhancing search result quality and performance.
- By smartly managing filter selectivity and integrating efficient vector search, AlloyDB ensures high-quality search results as data and workloads evolve.
- AlloyDB's ScaNN-powered vector search with adaptive filtration is recommended for real-world applications, offering advanced search capabilities across structured and unstructured data.
- Start leveraging AlloyDB’s ScaNN index for vector search and explore the adaptive filtration feature to enhance the quality and performance of your search queries today.
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