The `sparse_encoding` processor is used to generate a sparse vector/token and weights from text fields for [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/) using sparse retrieval.
Before using the `sparse_encoding` processor, you must set up a machine learning (ML) model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
`model_id` | String | Required | The ID of the model that will be used to generate the embeddings. The model must be deployed in OpenSearch before it can be used in neural search. For more information, see [Using custom models within OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/using-ml-models/) and [Sparse search]({{site.url}}{{site.baseurl}}/search-plugins/sparse-search/).
Follow these steps to use the processor in a pipeline. You must provide a model ID when creating the processor. For more information, see [Using custom models within OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/using-ml-models/).
The following example request creates an ingest pipeline where the text from `passage_text` will be converted into text embeddings and the embeddings will be stored in `passage_embedding`:
```json
PUT /_ingest/pipeline/nlp-ingest-pipeline
{
"description": "A sparse encoding ingest pipeline",
"processors": [
{
"sparse_encoding": {
"model_id": "aP2Q8ooBpBj3wT4HVS8a",
"field_map": {
"passage_text": "passage_embedding"
}
}
}
]
}
```
{% include copy-curl.html %}
**Step 2 (Optional): Test the pipeline.**
It is recommended that you test your pipeline before you ingest documents.
{: .tip}
To test the pipeline, run the following query:
```json
POST _ingest/pipeline/nlp-ingest-pipeline/_simulate
{
"docs": [
{
"_index": "testindex1",
"_id": "1",
"_source":{
"passage_text": "hello world"
}
}
]
}
```
{% include copy-curl.html %}
#### Response
The response confirms that in addition to the `passage_text` field, the processor has generated text embeddings in the `passage_embedding` field:
- To learn how to use the `neural_sparse` query for a sparse search, see [Neural sparse query]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural-sparse/).
- To learn more about sparse search, see [Sparse search]({{site.url}}{{site.baseurl}}/search-plugins/sparse-search/).
- To learn more about using models in OpenSearch, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
- For a comprehensive example, see [Neural search tutorial]({{site.url}}{{site.baseurl}}/search-plugins/neural-search-tutorial/).