kolchfa-aws a97c719591
Add multimodal search/sparse search/pre- and post-processing function documentation (#5168)
* Add multimodal search documentation

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* Text image embedding processor

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* Add prerequisite

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* Change query text

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* Added bedrock connector tutorial and renamed ML TOC

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* Name changes and rewording

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* Change connector link

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* Change link

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* Implemented tech review comments

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* Link fix and field name fix

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* Add default text embedding preprocessing and post-processing functions

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* Add sparse search documentation

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* Fix links

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* Pre/post processing function tech review comments

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* Fix link

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* Sparse search tech review comments

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* 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

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* Add actual test sparse pipeline response

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* Added tested examples

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* Added model choice for sparse search

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* Remove Bedrock connector

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* Implemented tech review feedback

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* Add that the model must be deployed to neural search

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* Apply suggestions from code review

Co-authored-by: Nathan Bower <nbower@amazon.com>
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* Link fix

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* Add session token to sagemaker blueprint

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* Formatted bullet points the same way

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* Specified both model types in neural sparse query

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* Added more explanation for default pre/post-processing functions

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* Remove framework and extensibility references

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* Minor rewording

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---------

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

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Markdown

---
layout: default
title: Neural query enricher
nav_order: 12
has_children: false
parent: Search processors
grand_parent: Search pipelines
---
# Neural query enricher processor
The `neural_query_enricher` search request processor is designed to set a default machine learning (ML) model ID at the index or field level for [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/) queries. To learn more about ML models, see [Using custom models within OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/ml-framework/).
## Request fields
The following table lists all available request fields.
Field | Data type | Description
:--- | :--- | :---
`default_model_id` | String | The model ID of the default model for an index. Optional. You must specify at least one `default_model_id` or `neural_field_default_id`. If both are provided, `neural_field_default_id` takes precedence.
`neural_field_default_id` | Object | A map of key-value pairs representing document field names and their associated default model IDs. Optional. You must specify at least one `default_model_id` or `neural_field_default_id`. If both are provided, `neural_field_default_id` takes precedence.
`tag` | String | The processor's identifier. Optional.
`description` | String | A description of the processor. Optional.
## Example
The following example request creates a search pipeline with a `neural_query_enricher` search request processor. The processor sets a default model ID at the index level and provides different default model IDs for two specific fields in the index:
```json
PUT /_search/pipeline/default_model_pipeline
{
"request_processors": [
{
"neural_query_enricher" : {
"tag": "tag1",
"description": "Sets the default model ID at index and field levels",
"default_model_id": "u5j0qYoBMtvQlfhaxOsa",
"neural_field_default_id": {
"my_field_1": "uZj0qYoBMtvQlfhaYeud",
"my_field_2": "upj0qYoBMtvQlfhaZOuM"
}
}
}
]
}
```
{% include copy-curl.html %}