Add documentation for k-NN clear cache api (#7030)

* Add documentation for k-NN clear cache api Signed-off-by: Naveen Tatikonda <[email protected]> * Update _search-plugins/knn/api.md Signed-off-by: Melissa Vagi <[email protected]> * Update api.md Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Signed-off-by: Melissa Vagi <[email protected]> * Update api.md Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Signed-off-by: Melissa Vagi <[email protected]> * Update _search-plugins/knn/api.md Signed-off-by: Melissa Vagi <[email protected]> * Update api.md Signed-off-by: Melissa Vagi <[email protected]> * Fix broken links Signed-off-by: Melissa Vagi <[email protected]> * Fix broken links Signed-off-by: Melissa Vagi <[email protected]> --------- Signed-off-by: Naveen Tatikonda <[email protected]> Signed-off-by: Melissa Vagi <[email protected]> Co-authored-by: Melissa Vagi <[email protected]> Co-authored-by: Nathan Bower <[email protected]>
opensearch-project · May 7, 2024 · d7993ad · d7993ad
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diff --git a/_field-types/supported-field-types/knn-vector.md b/_field-types/supported-field-types/knn-vector.md
@@ -68,7 +68,7 @@ PUT test-index
 ## Model IDs
 
 Model IDs are used when the underlying Approximate k-NN algorithm requires a training step. As a prerequisite, the
-model has to be created with the [Train API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-model). The
+model has to be created with the [Train API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-a-model). The
 model contains the information needed to initialize the native library segment files.
 
 ```json

diff --git a/_search-plugins/knn/api.md b/_search-plugins/knn/api.md
diff --git a/_search-plugins/knn/approximate-knn.md b/_search-plugins/knn/approximate-knn.md
@@ -133,7 +133,7 @@ indicates how many results the query actually returns. The plugin returns `k` am
 
 ### Building a k-NN index from a model
 
-For some of the algorithms that we support, the native library index needs to be trained before it can be used. It would be expensive to training every newly created segment, so, instead, we introduce the concept of a *model* that is used to initialize the native library index during segment creation. A *model* is created by calling the [Train API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-model), passing in the source of training data as well as the method definition of the model. Once training is complete, the model will be serialized to a k-NN model system index. Then, during indexing, the model is pulled from this index to initialize the segments.
+For some of the algorithms that we support, the native library index needs to be trained before it can be used. It would be expensive to training every newly created segment, so, instead, we introduce the concept of a *model* that is used to initialize the native library index during segment creation. A *model* is created by calling the [Train API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-a-model), passing in the source of training data as well as the method definition of the model. Once training is complete, the model will be serialized to a k-NN model system index. Then, during indexing, the model is pulled from this index to initialize the segments.
 
 To train a model, we first need an OpenSearch index with training data in it. Training data can come from
 any `knn_vector` field that has a dimension matching the dimension of the model you want to create. Training data can be the same data that you are going to index or have in a separate set. Let's create a training index:

diff --git a/_search-plugins/knn/knn-index.md b/_search-plugins/knn/knn-index.md
@@ -72,7 +72,7 @@ For the ARM64 architecture, only one performance-boosting Faiss library (`libope
 
 ## Method definitions
 
-A method definition refers to the underlying configuration of the approximate k-NN algorithm you want to use. Method definitions are used to either create a `knn_vector` field (when the method does not require training) or [create a model during training]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-model) that can then be used to [create a `knn_vector` field]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/#building-a-k-nn-index-from-a-model).
+A method definition refers to the underlying configuration of the approximate k-NN algorithm you want to use. Method definitions are used to either create a `knn_vector` field (when the method does not require training) or [create a model during training]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-a-model) that can then be used to [create a `knn_vector` field]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/#building-a-k-nn-index-from-a-model).
 
 A method definition will always contain the name of the method, the space_type the method is built for, the engine
 (the library) to use, and a map of parameters.
@@ -137,7 +137,7 @@ For more information about setting these parameters, refer to the [Faiss documen
 
 #### IVF training requirements
 
-The IVF algorithm requires a training step. To create an index that uses IVF, you need to train a model with the [Train API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-model), passing the IVF method definition. IVF requires that, at a minimum, there are `nlist` training data points, but it is [recommended that you use more than this](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#how-big-is-the-dataset). Training data can be composed of either the same data that is going to be ingested or a separate dataset.
+The IVF algorithm requires a training step. To create an index that uses IVF, you need to train a model with the [Train API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-a-model), passing the IVF method definition. IVF requires that, at a minimum, there are `nlist` training data points, but it is [recommended that you use more than this](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#how-big-is-the-dataset). Training data can be composed of either the same data that is going to be ingested or a separate dataset.
 
 ### Supported Lucene methods
 

diff --git a/_search-plugins/multimodal-search.md b/_search-plugins/multimodal-search.md
@@ -108,7 +108,7 @@ Before the document is ingested into the index, the ingest pipeline runs the `te
 
 ## Step 4: Search the index using neural search
 
-To perform vector search on your index, use the `neural` query clause either in the [k-NN plugin API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api/#search-model) or [Query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/index/) queries. You can refine the results by using a [k-NN search filter]({{site.url}}{{site.baseurl}}/search-plugins/knn/filter-search-knn/). You can search by text, image, or both text and image.
+To perform vector search on your index, use the `neural` query clause either in the [k-NN plugin API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api/#search-for-a-model) or [Query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/index/) queries. You can refine the results by using a [k-NN search filter]({{site.url}}{{site.baseurl}}/search-plugins/knn/filter-search-knn/). You can search by text, image, or both text and image.
 
 The following example request uses a neural query to search for text and image:
 

diff --git a/_search-plugins/semantic-search.md b/_search-plugins/semantic-search.md
@@ -115,7 +115,7 @@ Before the document is ingested into the index, the ingest pipeline runs the `te
 
 ## Step 4: Search the index using neural search
 
-To perform vector search on your index, use the `neural` query clause either in the [k-NN plugin API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api/#search-model) or [Query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/index/) queries. You can refine the results by using a [k-NN search filter]({{site.url}}{{site.baseurl}}/search-plugins/knn/filter-search-knn/).
+To perform vector search on your index, use the `neural` query clause either in the [k-NN plugin API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api/#search-for-a-model) or [Query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/index/) queries. You can refine the results by using a [k-NN search filter]({{site.url}}{{site.baseurl}}/search-plugins/knn/filter-search-knn/).
 
 The following example request uses a Boolean query to combine a filter clause and two query clauses---a neural query and a `match` query. The `script_score` query assigns custom weights to the query clauses: