opensearch-docs-cn/_api-reference/ingest-apis/processors/sparse-encoding.md

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Add multimodal search/sparse search/pre- and post-processing function documentation (#5168) * Add multimodal search documentation Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Text image embedding processor Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add prerequisite Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change query text Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added bedrock connector tutorial and renamed ML TOC Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Name changes and rewording Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change connector link Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Change link Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Implemented tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Link fix and field name fix Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add default text embedding preprocessing and post-processing functions Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add sparse search documentation Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Fix links Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Pre/post processing function tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Fix link Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Sparse search tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented doc review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add actual test sparse pipeline response Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added tested examples Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added model choice for sparse search Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Remove Bedrock connector Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Implemented tech review feedback Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add that the model must be deployed to neural search Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Nathan Bower <nbower@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Link fix Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add session token to sagemaker blueprint Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Formatted bullet points the same way Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Specified both model types in neural sparse query Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added more explanation for default pre/post-processing functions Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Remove framework and extensibility references Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Minor rewording Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> --------- Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Co-authored-by: Nathan Bower <nbower@amazon.com>
2023-10-16 10:45:35 -04:00
---
layout: default
title: Sparse encoding
parent: Ingest processors
grand_parent: Ingest APIs
nav_order: 240
---
# Sparse encoding
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.
**PREREQUISITE**<br>
Before using the `sparse_encoding` processor, you must set up a machine learning (ML) model. For more information, see [Using custom models within OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/ml-framework/) and [Semantic search]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search/).
{: .note}
The following is the syntax for the `sparse_encoding` processor:
```json
{
"sparse_encoding": {
"model_id": "<model_id>",
"field_map": {
"<input_field>": "<vector_field>"
}
}
}
```
{% include copy-curl.html %}
#### Configuration parameters
The following table lists the required and optional parameters for the `sparse_encoding` processor.
| Name | Data type | Required | Description |
|:---|:---|:---|:---|
`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/ml-framework/) and [Semantic search]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search/).
`field_map` | Object | Required | Contains key-value pairs that specify the mapping of a text field to a `rank_features` field.
`field_map.<input_field>` | String | Required | The name of the field from which to obtain text for generating vector embeddings.
`field_map.<vector_field>` | String | Required | The name of the vector field in which to store the generated vector embeddings.
`description` | String | Optional | A brief description of the processor. |
`tag` | String | Optional | An identifier tag for the processor. Useful for debugging to distinguish between processors of the same type. |
## Using the processor
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/ml-framework/).
**Step 1: Create a pipeline.**
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:
```json
{
"docs" : [
{
"doc" : {
"_index" : "testindex1",
"_id" : "1",
"_source" : {
"passage_embedding" : {
"!" : 0.8708904,
"door" : 0.8587369,
"hi" : 2.3929274,
"worlds" : 2.7839446,
"yes" : 0.75845814,
"##world" : 2.5432441,
"born" : 0.2682308,
"nothing" : 0.8625516,
"goodbye" : 0.17146169,
"greeting" : 0.96817183,
"birth" : 1.2788506,
"come" : 0.1623208,
"global" : 0.4371151,
"it" : 0.42951578,
"life" : 1.5750692,
"thanks" : 0.26481047,
"world" : 4.7300377,
"tiny" : 0.5462298,
"earth" : 2.6555297,
"universe" : 2.0308156,
"worldwide" : 1.3903781,
"hello" : 6.696973,
"so" : 0.20279501,
"?" : 0.67785245
},
"passage_text" : "hello world"
},
"_ingest" : {
"timestamp" : "2023-10-11T22:35:53.654650086Z"
}
}
}
]
}
```
## Next steps
- 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 neural search, see [Sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/).
- To learn more about using models in OpenSearch, see [Using custom models within OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/ml-framework/).
- For a semantic search tutorial, see [Semantic search]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search/).