tutorial: generate embedding for arrays of object (#2477) (#2478)

Signed-off-by: Yaliang Wu <[email protected]>
(cherry picked from commit 0722df1)

Co-authored-by: Yaliang Wu <[email protected]>

docs/tutorials/semantic_search/generate_embeddings_for_arrays_of_object.md

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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"}] }
+
+```