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fix: grammar and simplifying tutorial
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Signed-off-by: Brian Flores <[email protected]>
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Expand Up @@ -6,33 +6,33 @@ grand_parent: Search relevance
has_children: false
nav_order: 20
---
# ML Inference Processor with By Field Rerank type
# ML Inference processor with By Field rerank type

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[OpenSearch.HeadingCapitalization] 'ML Inference processor with By Field rerank type' is a heading and should be in sentence case.

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{"message": "[OpenSearch.HeadingCapitalization] 'ML Inference processor with By Field rerank type' is a heading and should be in sentence case.", "location": {"path": "_search-plugins/search-relevance/ml-inference-rerank-by-field.md", "range": {"start": {"line": 9, "column": 3}}}, "severity": "ERROR"}
Introduced 2.18
{: .label .label-purple }

You can use the results of a remote model via the [ml_inference]({{site.url}}{{site.baseurl}}/_ingest-pipelines/processors/ml-inference.md) processor, with a [by_field]({{site.url}}{{site.baseurl}}/search-plugins/search-relevance/rerank-by-field/) rerank type to get better search results.
You can use the results of a ml model using the [ml_inference]({{site.url}}{{site.baseurl}}/_ingest-pipelines/processors/ml-inference.md) processor, with a [by_field]({{site.url}}{{site.baseurl}}/search-plugins/search-relevance/rerank-by-field/) rerank type to get better search results.
In order to do this you need to configure a search pipeline that runs at search time. The search pipeline will intercept search results
pass them to the ml_inference processor which will apply a remote cross encoder model. Then once the results are returned it will apply the
reranker to use that metric in order to rerank your documents.

In this tutorial we will showcase a scenario with documents related to New York City areas with emphasis on finding better search results based
on the provided search query. We will use [Huggingface cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2)
on the provided search query. We will use [HuggingFace cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2)

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[OpenSearch.SubstitutionsError] Use 'Hugging Face' instead of 'HuggingFace'.

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{"message": "[OpenSearch.SubstitutionsError] Use 'Hugging Face' instead of 'HuggingFace'.", "location": {"path": "_search-plugins/search-relevance/ml-inference-rerank-by-field.md", "range": {"start": {"line": 19, "column": 44}}}, "severity": "ERROR"}
hosted on Amazon SageMaker.

## Running a search with both processors

To run a search with reranking, follow these steps:

0. [Deploy the model on Amazon SageMaker](#0-deploy-the-model-on-amazon-sagemaker)
0. [Deploy the model on Amazon SageMaker.](#0-deploy-the-model-on-amazon-sagemaker)
1. [Create an index for ingestion](#step-1-create-an-index-for-ingestion).
2. [Create a connector](#step-2-create-a-connector).
3. [Create a model](#step-3-create-a-model).
4. [Create the Search pipeline](#step-4-create-the-search-pipeline).
5. [apply the pipeline on a search query](#step-5-apply-the-pipeline-on-a-search-query).

## 0. Deploy the model on Amazon Sagemaker
Use the following code to deploy the model on Amazon Sagemaker.
You can find all supported instance type and price on [Amazon Sagemaker Pricing document](https://aws.amazon.com/sagemaker/pricing/). Suggest to use GPU for better performance.
## 0. Deploy the model on Amazon SageMaker
Use the following code to deploy the model on Amazon SageMaker.
You can find all supported instance type and price on [Amazon SageMaker Pricing document](https://aws.amazon.com/sagemaker/pricing/). Suggest to use GPU for better performance.
```python
import sagemaker
import boto3
Expand All @@ -59,7 +59,7 @@ predictor = huggingface_model.deploy(
```
To find the endpoint make sure to the SageMaker homepage and navigate in the left tab **Inference > Endpoints** make note of the url specific to the model created it will be used when creating the connector.

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[OpenSearch.Spelling] Error: url. If you are referencing a setting, variable, format, function, or repository, surround it with tic marks.

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{"message": "[OpenSearch.Spelling] Error: url. If you are referencing a setting, variable, format, function, or repository, surround it with tic marks.", "location": {"path": "_search-plugins/search-relevance/ml-inference-rerank-by-field.md", "range": {"start": {"line": 60, "column": 130}}}, "severity": "ERROR"}

## Step 1: Create an Index for Ingestion
## Step 1: Create an index for ingestion
Create an index called nyc_areas
```json
POST /nyc_areas/_bulk
Expand All @@ -80,13 +80,13 @@ POST /nyc_areas/_bulk
{% include copy-curl.html %}

## Step 2: Create a connector
Create a conector assuming you have created a sagemaker model with a cross encoder
Create a connector assuming you have created a SageMaker model with a cross encoder

```json
POST /_plugins/_ml/connectors/_create
{
"name": "SageMaker cross-encoder model",
"description": "Test connector for Sagemaker cross-encoder model",
"description": "Test connector for SageMaker cross-encoder hosted model",
"version": 1,
"protocol": "aws_sigv4",
"credential": {
Expand All @@ -95,7 +95,7 @@ POST /_plugins/_ml/connectors/_create
"session_token": "<Session token>"
},
"parameters": {
"region": "us-east-1",
"region": "<region>",
"service_name": "sagemaker"
},
"actions": [
Expand All @@ -106,18 +106,7 @@ POST /_plugins/_ml/connectors/_create
"headers": {
"content-type": "application/json"
},
"request_body": "{ \"inputs\":${parameters.inputs}}" ,
"pre_process_function":
"""
def json = params.json;
def inputs = json.parameters.inputs;

def result = [:];
result.query_text = inputs.text;
result.text_docs = [inputs.text_pair];

return result;
"""
"request_body": "{ \"inputs\": { \"text\": \"${parameters.text}\", \"text_pair\": \"${parameters.text_pair}\" }}"
}
]
}
Expand All @@ -129,10 +118,10 @@ POST /_plugins/_ml/connectors/_create
```json
POST /_plugins/_ml/models/_register
{
"name": "text classification model",
"name": "Cross encoder model",
"version": "1.0.1",
"function_name": "remote",
"description": "Text Classification",
"description": "Using a SageMaker to apply a cross encoder model",
"connector_id": "<connector_id_from_step_2>"
}

Expand All @@ -157,7 +146,6 @@ PUT /_search/pipeline/my_pipeline
"tag": "ml_inference",
"description": "This processor runs ml inference during search response",
"model_id": "<model_id_from_step_3>",
"model_input":"""{"parameters":{"inputs":{"text":"${input_map.text}","text_pair":"${input_map.text_pair}"}}}""",
"function_name": "REMOTE",
"input_map": [
{
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