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[Backport 2.x] tutorial: generate embedding for arrays of object #2478

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# Topic

This tutorial shows how to generate embeddings for arrays of objects in OpenSearch.

Note: Replace the placeholders that start with `your_` with your own values.

# Steps

## 1. Create embedding model

We will use [Bedrock Titan Embedding model](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) in this tutorial.

- If you are using AWS managed OpenSearch service, you can use this [python notebook](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/tutorials/aws/AIConnectorHelper.ipynb) to create Bedrock Embedding Model easily. Search `1. Create Connector of Bedrock Embedding Model` on the page.
Or you can manually create connector following this [tutorial](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/tutorials/aws/semantic_search_with_bedrock_titan_embedding_model.md).

- If you are using self-managed OpenSearch, you can follow this [blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/bedrock_connector_titan_embedding_blueprint.md).

Use the model ID from the response to test predict API:
```
POST /_plugins/_ml/models/your_embedding_model_id/_predict
{
"parameters": {
"inputText": "hello world"
}
}
```
Sample response:

```
{
"inference_results": [
{
"output": [
{
"name": "sentence_embedding",
"data_type": "FLOAT32",
"shape": [ 1536 ],
"data": [0.7265625, -0.0703125, 0.34765625, ...]
}
],
"status_code": 200
}
]
}
```

## 2. Create ingest pipeline

### 2.1 Create test index
```
PUT my_books
{
"settings" : {
"index.knn" : "true",
"default_pipeline": "bedrock_embedding_foreach_pipeline"
},
"mappings": {
"properties": {
"books": {
"type": "nested",
"properties": {
"title_embedding": {
"type": "knn_vector",
"dimension": 1536
},
"title": {
"type": "text"
},
"description": {
"type": "text"
}
}
}
}
}
}
```

### 2.2 Create ingest pipeline

Create sub-pipeline to generate embedding for one item in the array.

This pipeline contains 3 processors
- set processor: The `text_embedding` processor is unable to identify "_ingest._value.title". You need to copy "_ingest._value.title" to a temporary field for text_embedding to process it.
- text_embedding processor: convert value of the temporary field to embedding
- remove processor: remove temporary field
```
PUT _ingest/pipeline/bedrock_embedding_pipeline
{
"processors": [
{
"set": {
"field": "title_tmp",
"value": "{{_ingest._value.title}}"
}
},
{
"text_embedding": {
"model_id": your_embedding_model_id,
"field_map": {
"title_tmp": "_ingest._value.title_embedding"
}
}
},
{
"remove": {
"field": "title_tmp"
}
}
]
}
```

Create pipeline with foreach processor:
```
PUT _ingest/pipeline/bedrock_embedding_foreach_pipeline
{
"description": "Test nested embeddings",
"processors": [
{
"foreach": {
"field": "books",
"processor": {
"pipeline": {
"name": "bedrock_embedding_pipeline"
}
},
"ignore_failure": true
}
}
]
}
```

### 2.3 Simulate pipeline

- Case1: two book objects with title
```
POST _ingest/pipeline/bedrock_embedding_foreach_pipeline/_simulate
{
"docs": [
{
"_index": "my_books",
"_id": "1",
"_source": {
"books": [
{
"title": "first book",
"description": "This is first book"
},
{
"title": "second book",
"description": "This is second book"
}
]
}
}
]
}
```
Response
```
{
"docs": [
{
"doc": {
"_index": "my_books",
"_id": "1",
"_source": {
"books": [
{
"title": "first book",
"title_embedding": [-1.1015625, 0.65234375, 0.7578125, ...],
"description": "This is first book"
},
{
"title": "second book",
"title_embedding": [-0.65234375, 0.21679688, 0.7265625, ...],
"description": "This is second book"
}
]
},
"_ingest": {
"_value": null,
"timestamp": "2024-05-28T16:16:50.538929413Z"
}
}
}
]
}
```
- Case2: book object without title
```
POST _ingest/pipeline/bedrock_embedding_foreach_pipeline/_simulate
{
"docs": [
{
"_index": "my_books",
"_id": "1",
"_source": {
"books": [
{
"title": "first book",
"description": "This is first book"
},
{
"description": "This is second book"
}
]
}
}
]
}
```
Response
```
{
"docs": [
{
"doc": {
"_index": "my_books",
"_id": "1",
"_source": {
"books": [
{
"title": "first book",
"title_embedding": [-1.1015625, 0.65234375, 0.7578125, ...],
"description": "This is first book"
},
{
"title": "second book",
"description": "This is second book"
}
]
},
"_ingest": {
"_value": null,
"timestamp": "2024-05-28T16:19:03.942644042Z"
}
}
}
]
}
```
### 2.4 Test ingest data
Ingest one doc
```
PUT my_books/_doc/1
{
"books": [
{
"title": "first book",
"description": "This is first book"
},
{
"title": "second book",
"description": "This is second book"
}
]
}
```
Get document
```
GET my_books/_doc/1
```
Response
```
{
"_index": "my_books",
"_id": "1",
"_version": 1,
"_seq_no": 0,
"_primary_term": 1,
"found": true,
"_source": {
"books": [
{
"description": "This is first book",
"title": "first book",
"title_embedding": [-1.1015625, 0.65234375, 0.7578125, ...]
},
{
"description": "This is second book",
"title": "second book",
"title_embedding": [-0.65234375, 0.21679688, 0.7265625, ...]
}
]
}
}
```
Bulk ingestion
```
POST _bulk
{ "index" : { "_index" : "my_books" } }
{ "books" : [{"title": "first book", "description": "This is first book"}, {"title": "second book", "description": "This is second book"}] }
{ "index" : { "_index" : "my_books" } }
{ "books" : [{"title": "third book", "description": "This is third book"}, {"description": "This is fourth book"}] }

```
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