[Backport 2.16] Refactor of the neural sparse search tutorial

_ingest-pipelines/processors/sparse-encoding.md

@@ -141,7 +141,7 @@ The response confirms that in addition to the `passage_text` field, the processo
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 ```
 
-Once you have created an ingest pipeline, you need to create an index for ingestion and ingest documents into the index. To learn more, see [Step 2: Create an index for ingestion]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/#step-2-create-an-index-for-ingestion) and [Step 3: Ingest documents into the index]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/#step-3-ingest-documents-into-the-index) of [Neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/).
+Once you have created an ingest pipeline, you need to create an index for ingestion and ingest documents into the index. To learn more, see [Create an index for ingestion]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-with-pipelines/#step-2b-create-an-index-for-ingestion) and [Step 3: Ingest documents into the index]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-with-pipelines/#step-2c-ingest-documents-into-the-index) of [Neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/).
 
 ---
 

_ml-commons-plugin/pretrained-models.md

@@ -46,19 +46,21 @@ The following table provides a list of sentence transformer models and artifact
 
 Sparse encoding models transfer text into a sparse vector and convert the vector to a list of `<token: weight>` pairs representing the text entry and its corresponding weight in the sparse vector. You can use these models for use cases such as clustering or sparse neural search.
 
-We recommend the following models for optimal performance:
+We recommend the following combinations for optimal performance:
 
 - Use the `amazon/neural-sparse/opensearch-neural-sparse-encoding-v1` model during both ingestion and search.
 - Use the `amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1` model during ingestion and the
-`amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1` model during search.
+`amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1` tokenizer during search.
+
+For more information about the preceding options for running neural sparse search, see [Generating sparse vector embeddings within OpenSearch]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-with-pipelines/).
 
 The following table provides a list of sparse encoding models and artifact links you can use to download them.
 
 | Model name | Version | Auto-truncation | TorchScript artifact | Description |
 |:---|:---|:---|:---|:---|
 | `amazon/neural-sparse/opensearch-neural-sparse-encoding-v1` | 1.0.1 | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-v1/1.0.1/torch_script/neural-sparse_opensearch-neural-sparse-encoding-v1-1.0.1-torch_script.zip)<br>- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-v1/1.0.1/torch_script/config.json) | A neural sparse encoding model. The model transforms text into a sparse vector, identifies the indexes of non-zero elements in the vector, and then converts the vector into `<entry, weight>` pairs, where each entry corresponds to a non-zero element index. To experiment with this model using transformers and the PyTorch API, see the [HuggingFace documentation](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1). |
 | `amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1` | 1.0.1 | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1/1.0.1/torch_script/neural-sparse_opensearch-neural-sparse-encoding-doc-v1-1.0.1-torch_script.zip)<br>- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1/1.0.1/torch_script/config.json) | A neural sparse encoding model. The model transforms text into a sparse vector, identifies the indexes of non-zero elements in the vector, and then converts the vector into `<entry, weight>` pairs, where each entry corresponds to a non-zero element index. To experiment with this model using transformers and the PyTorch API, see the [HuggingFace documentation](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1). |
-| `amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1` | 1.0.1 | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.1/torch_script/neural-sparse_opensearch-neural-sparse-tokenizer-v1-1.0.1-torch_script.zip)<br>- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.1/torch_script/config.json) | A neural sparse tokenizer model. The model tokenizes text into tokens and assigns each token a predefined weight, which is the token's inverse document frequency (IDF). If the IDF file is not provided, the weight defaults to 1. For more information, see [Preparing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/custom-local-models/#preparing-a-model). |
+| `amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1` | 1.0.1 | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.1/torch_script/neural-sparse_opensearch-neural-sparse-tokenizer-v1-1.0.1-torch_script.zip)<br>- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.1/torch_script/config.json) | A neural sparse tokenizer. The tokenizer splits text into tokens and assigns each token a predefined weight, which is the token's inverse document frequency (IDF). If the IDF file is not provided, the weight defaults to 1. For more information, see [Preparing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/custom-local-models/#preparing-a-model). |
 
 ### Cross-encoder models
 **Introduced 2.12**