opensearch-docs-cn/_ingest-pipelines/processors/text-embedding.md

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Text embedding

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

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

The following is the syntax for the text_embedding processor:

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

{% include copy-curl.html %}

Configuration parameters

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

Name Data type Required 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"
        }
      }
    }
  ]
}

{% include copy-curl.html %}

Step 2 (Optional): Test the pipeline.

It is recommended that you test your pipeline before you ingest documents. {: .tip}

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"
      }
    }
  ]
}

{% include copy-curl.html %}

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"
        }
      }
    }
  ]
}

Next steps