This page describes AlloyDB AI vector search strategies and explains when to use each strategy. By default, AlloyDB uses k-nearest neighbors search (KNN) to find vectors that are similar to a query. Vector indexes implement a search strategy called Approximate Nearest Neighbor (ANN). When you create a vector index, AlloyDB AI uses ANN, which provides better performance than KNN. Keep in mind that, when you select a vector index, you need to balance query latency and recall.
Recall measures how effectively a search retrieves all relevant items for a given query. For example, imagine you have 100 embeddings, each one representing an entity in your database. You query your embeddings with a target vector and limit it to 10 results. A KNN vector search finds the 10 exact closest vectors using a brute force calculation method, which results in 100% recall. AlloyDB AI uses this method by default if no vector search index is created or chosen. When you create a vector index in AlloyDB for PostgreSQL, it typically uses ANN, which might partition vectors according to similarity to facilitate faster retrieval. As a result, using ANN, the 10 vectors returned in the earlier example might not be exactly the 10 vectors that are closest in distance. If only 8 out of the 10 retrieved vectors are the closest in space to your query vector, then your recall is 80%.
Query latency defines how fast the search results are generated. For example, latency is calculated based on the time spent on a search to return the vectors after you submit a query.
Choose your search strategy
When you perform vector search in AlloyDB, choose one the following search strategies:
| Search Strategy | Description | Use Cases |
| K-nearest neighbors (KNN) | An algorithm that finds the k-nearest neighbors data points to a given query data point. When you perform a vector search without creating an index, a KNN search is performed by default. |
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| Approximate Nearest Neighbors (ANN) | An algorithm that finds approximately the closest data points. ANN divides existing customer data points into small groups based on similarities. |
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Google recommends that you create a vector index to optimize performance on your vector search queries. For more information about how the ANN index is used for similarity searches, see Create indexes using ScaNN.