You're viewing version 2.16 of the OpenSearch documentation. This version is no longer maintained. For the latest version, see the current documentation. For information about OpenSearch version maintenance, see Release Schedule and Maintenance Policy.
k-NN settings
The k-NN plugin adds several new cluster settings. To learn more about static and dynamic settings, see Configuring OpenSearch.
Cluster settings
The following table lists all available cluster-level k-NN settings. For more information about cluster settings, see Configuring OpenSearch and Updating cluster settings using the API.
| Setting | Static/Dynamic | Default | Description |
|---|---|---|---|
knn.plugin.enabled | Dynamic | true | Enables or disables the k-NN plugin. |
knn.algo_param.index_thread_qty | Dynamic | 1 | The number of threads used for native library index creation. Keeping this value low reduces the CPU impact of the k-NN plugin but also reduces indexing performance. |
knn.cache.item.expiry.enabled | Dynamic | false | Whether to remove native library indexes that have not been accessed for a certain duration from memory. |
knn.cache.item.expiry.minutes | Dynamic | 3h | If enabled, the amount of idle time before a native library index is removed from memory. |
knn.circuit_breaker.unset.percentage | Dynamic | 75 | The native memory usage threshold for the circuit breaker. Memory usage must be lower than this percentage of knn.memory.circuit_breaker.limit in order for knn.circuit_breaker.triggered to remain false. |
knn.circuit_breaker.triggered | Dynamic | false | True when memory usage exceeds the knn.circuit_breaker.unset.percentage value. |
knn.memory.circuit_breaker.limit | Dynamic | 50% | The native memory limit for native library indexes. At the default value, if a machine has 100 GB of memory and the JVM uses 32 GB, then the k-NN plugin uses 50% of the remaining 68 GB (34 GB). If memory usage exceeds this value, then the plugin removes the native library indexes used least recently. |
knn.memory.circuit_breaker.enabled | Dynamic | true | Whether to enable the k-NN memory circuit breaker. |
knn.model.index.number_of_shards | Dynamic | 1 | The number of shards to use for the model system index, which is the OpenSearch index that stores the models used for approximate nearest neighbor (ANN) search. |
knn.model.index.number_of_replicas | Dynamic | 1 | The number of replica shards to use for the model system index. Generally, in a multi-node cluster, this value should be at least 1 in order to increase stability. |
knn.model.cache.size.limit | Dynamic | 10% | The model cache limit cannot exceed 25% of the JVM heap. |
knn.faiss.avx2.disabled | Static | false | A static setting that specifies whether to disable the SIMD-based libopensearchknn_faiss_avx2.so library and load the non-optimized libopensearchknn_faiss.so library for the Faiss engine on machines with x64 architecture. For more information, see SIMD optimization for the Faiss engine. |
Index settings
The following table lists all available index-level k-NN settings. All settings are static. For information about updating static index-level settings, see Updating a static index setting.
| Setting | Default | Description |
|---|---|---|
index.knn.advanced.filtered_exact_search_threshold | null | The filtered ID threshold value used to switch to exact search during filtered ANN search. If the number of filtered IDs in a segment is lower than this setting’s value, then exact search will be performed on the filtered IDs. |
index.knn.algo_param.ef_search | 100 | ef (or efSearch) represents the size of the dynamic list for the nearest neighbors used during a search. Higher ef values lead to a more accurate but slower search. ef cannot be set to a value lower than the number of queried nearest neighbors, k. ef can take any value between k and the size of the dataset. |