Add new indexing parameter and update performance tuning instruction

Signed-off-by: Vijayan Balasubramanian <balasvij@amazon.com>

Author: VijayanB

_search-plugins/knn/knn-index.md

@@ -368,6 +368,7 @@ Setting | Default | Updatable | Description
 :--- | :--- | :--- | :---
 `index.knn` | false | false | Whether the index should build native library indexes for the `knn_vector` fields. If set to false, the `knn_vector` fields will be stored in doc values, but approximate k-NN search functionality will be disabled.
 `index.knn.algo_param.ef_search` | 100 | true | The size of the dynamic list used during k-NN searches. Higher values result in more accurate but slower searches. Only available for NMSLIB.
+`index.knn.advanced.approximate_threshold` | 15000   | true      | This threshold defines the minimum number of vectors required in segment, before creating specialized vector data structures for approximate search. Set `-1` to disable building vector data structures and `0` to build always.
 `index.knn.algo_param.ef_construction` | 100 | false | Deprecated in 1.0.0. Instead, use the [mapping parameters](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#method-definitions) to set this value.
 `index.knn.algo_param.m` | 16 | false | Deprecated in 1.0.0. Use the [mapping parameters](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#method-definitions) to set this value instead.
 `index.knn.space_type` | l2 | false | Deprecated in 1.0.0. Use the [mapping parameters](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#method-definitions) to set this value instead.

_search-plugins/knn/performance-tuning.md

@@ -98,6 +98,21 @@ In OpenSearch 2.15 or later, you can further improve indexing speed and reduce d
 This is an expert-level setting. Disabling the `_recovery_source` may lead to failures during peer-to-peer recovery. Before disabling the `_recovery_source`, check with your OpenSearch cluster admin to determine whether your cluster performs regular flushes before starting the peer-to-peer recovery of shards before disabling the `_recovery_source`.  
 {: .warning}
 
+### (Expert-level) Build vector data structures on demand
+
+This should be considered only for workloads where indexing happens as one initial bulk upload and will be available only for search after force merging to 1 segment.
+
+During indexing, vector search builds specialized data structure for a knn_vector field to support approximate neighbor search for efficiently finding k nearest neighbors. However, during `forcemerge`, k-NN indices rebuilds those data structures from beginning. Hence, to speed up indexing, you can update the index settings to improve overall indexing time
+
+
+* Either create an index or update with setting [index.knn.advanced.approximate_threshold]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index/#index-settings) to `-1` . This will disable building vector data structures for new segments.
+* Perform bulk indexing and, makes sure no search is performed while ingestion. If search is performed when vector data structures are disabled, exact search will be executed in the meantime.
+* Once the indexing is completed, reenable the setting by updating index.knn.advanced.approximate_threshold to `0`.
+* Perform force merge to max_segments = 1 to build vector data structure one time.
+* After force merge, new search request will always execute approximate k-nn search as expected.
+
+If you forgot to update the setting to 0 before force merge, you have to reindex data.
+
 ## Search performance tuning
 
 Take the following steps to improve search performance: