Link Search Menu Expand Document Documentation Menu
Documentation

You're viewing version 2.14 of the OpenSearch documentation. This version is no longer maintained. For the latest version, see the current documentation. For information about OpenSearch version maintenance, see Release Schedule and Maintenance Policy.

Text embedding processor

The text_embedding processor is used to generate vector embeddings from text fields for semantic search.

PREREQUISITE
Before using the text_embedding processor, you must set up a machine learning (ML) model. For more information, see Choosing a model.

The following is the syntax for the text_embedding processor:

{
  "text_embedding": {
    "model_id": "<model_id>",
    "field_map": {
      "<input_field>": "<vector_field>"
    }
  }
}

Configuration parameters

The following table lists the required and optional parameters for the text_embedding processor.

Parameter Data type Required/Optional 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 and Semantic search.
field_map Object Required Contains key-value pairs that specify the mapping of a text field to a vector field.
field_map.<input_field> String Required The name of the field from which to obtain text for generating text embeddings.
field_map.<vector_field> String Required The name of the vector field in which to store the generated text 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.

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:

PUT /_ingest/pipeline/nlp-ingest-pipeline
{
  "description": "A text embedding pipeline",
  "processors": [
    {
      "text_embedding": {
        "model_id": "bQ1J8ooBpBj3wT4HVUsb",
        "field_map": {
          "passage_text": "passage_embedding"
        }
      }
    }
  ]
}

Step 2 (Optional): Test the pipeline.

It is recommended that you test your pipeline before you ingest documents.

To test the pipeline, run the following query:

POST _ingest/pipeline/nlp-ingest-pipeline/_simulate
{
  "docs": [
    {
      "_index": "testindex1",
      "_id": "1",
      "_source":{
         "passage_text": "hello world"
      }
    }
  ]
}

Response

The response confirms that in addition to the passage_text field, the processor has generated text embeddings in the passage_embedding field:

{
  "docs": [
    {
      "doc": {
        "_index": "testindex1",
        "_id": "1",
        "_source": {
          "passage_embedding": [
            -0.048237972,
            -0.07612712,
            0.3262124,
            ...
            -0.16352308
          ],
          "passage_text": "hello world"
        },
        "_ingest": {
          "timestamp": "2023-10-05T15:15:19.691345393Z"
        }
      }
    }
  ]
}

Once you have created an ingest pipeline, you need to create an index for ingestion and ingest documents into the index. To learn more, see Step 2: Create an index for ingestion and Step 3: Ingest documents into the index of Semantic search.

Next steps

350 characters left

Have a question? .

Want to contribute? or .