Merge pull request #247 from jmazanec15/knn-update-1.8

Knn update 1.8

Author: aetter

docs/knn/index.md

@@ -8,18 +8,21 @@ has_toc: false
 
 # KNN
 
-Short for its associated *k-nearest neighbors* algorithm, the KNN plugin lets you search for points in a vector space and find the "nearest neighbors" for those points by Euclidean distance. Use cases include recommendations (for example, an "other songs you might like" feature in a music application), image recognition, and fraud detection. For background information on the algorithm, see [Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).
+Short for its associated *k-nearest neighbors* algorithm, the KNN plugin lets you search for points in a vector space and find the "nearest neighbors" for those points by Euclidean distance or cosine similarity. Use cases include recommendations (for example, an "other songs you might like" feature in a music application), image recognition, and fraud detection. For background information on the algorithm, see [Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).
 
 
 ## Get started
 
-To use the KNN plugin, you must create an index with the `index.knn` setting and add one or more fields of the `knn_vector` data type:
+To use the KNN plugin, you must create an index with the `index.knn` setting and add one or more fields of the `knn_vector` data type. Additionally, you can specify the `index.knn.space_type` with `l2` or `cosinesimil`, respectively, to use either Euclidean distance or cosine similarity for calculations. By default, `index.knn.space_type` is set to `l2`. Here is an example that creates an index with two knn_vector fields and uses cosine similarity:
 
 ```json
 PUT my-index
 {
   "settings": {
-    "index.knn": true
+    "index": {
+      "knn": true,
+      "knn.space_type": "cosinesimil"
+    }
   },
   "mappings": {
     "properties": {

docs/knn/settings.md

@@ -19,6 +19,7 @@ Setting | Default | Description
 `index.knn.algo_param.ef_search` | 512 | The size of the dynamic list used during KNN searches. Higher values lead to more accurate, but slower searches.
 `index.knn.algo_param.ef_construction` | 512 | The size of the dynamic list used during KNN graph creation. Higher values lead to a more accurate graph, but slower indexing speed.
 `index.knn.algo_param.m` | 16 | The number of bidirectional links that the plugin creates for each new element. Increasing and decreasing this value can have a large impact on memory consumption. Keep this value between 2-100.
+`index.knn.space_type` | "l2" | The vector space used to calculate the distance between vectors. Currently, the KNN plugin supports the `l2` space (Euclidean distance) and `cosinesimil` space (cosine similarity). For more information on these spaces, refer to the [NMSLIB documentation](https://github.com/nmslib/nmslib/blob/master/manual/spaces.md).
 
 
 ## Cluster settings