Neural search transforms text into vectors and facilitates vector search both at ingestion time and at search time. During ingestion, neural search transforms document text into vector embeddings and indexes both the text and its vector embeddings in a k-NN index. When you use a neural query during search, neural search converts the query text into vector embeddings, uses vector search to compare the query and document embeddings, and returns the closest results.
The Neural Search plugin comes bundled with OpenSearch and is generally available as of OpenSearch 2.9. For more information, see [Managing plugins]({{site.url}}{{site.baseurl}}/opensearch/install/plugins#managing-plugins).
To generate vector embeddings for text fields, you need to create a neural search [ingest pipeline]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/). An ingest pipeline consists of a series of processors that manipulate documents during ingestion, allowing the documents to be vectorized.
### Path and HTTP method
The following API operation creates a neural search ingest pipeline:
In the pipeline request body, you must set up a `text_embedding` processor (the only processor supported by neural search), which will convert the text in a document field to vector embeddings. The processor's `field_map` determines the input fields from which to generate vector embeddings and the output fields in which to store the embeddings:
```json
"text_embedding": {
"model_id": "<model_id>",
"field_map": {
"<input_field>": "<vector_field>"
}
}
```
The following table lists the `text_embedding` processor request fields.
`model_id` | String | The ID of the model that will be used to generate the embeddings. The model must be indexed in OpenSearch before it can be used in neural search. For more information, see [ML Framework]({{site.url}}{{site.baseurl}}/ml-commons-plugin/ml-framework/) and [Semantic search]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search/).
`field_map.<input_field>` | String | The name of the field from which to obtain text for generating text embeddings.
`field_map.<vector_field>` | String | The name of the vector field in which to store the generated text embeddings.
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`:
In order to use the text embedding processor defined in your pipelines, create a k-NN index with mapping data that aligns with the maps specified in your pipeline. For example, the `<vector_field>` defined in the `field_map` of your processor must be mapped as a k-NN vector field with a dimension that matches the model dimension. Similarly, the `<input_field>` defined in your processor should be mapped as `text` in your index.
For more information about creating a k-NN index and the methods it supports, see [k-NN index]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index/).
Before the document is ingested into the index, the ingest pipeline runs the `text_embedding` processor on the document, generating text embeddings for the `passage_text` field. The indexed document contains the `passage_text` field that has the original text and the `passage_embedding` field that has the vector embeddings.
To perform vector search on your index, use the `neural` query clause either in the [k-NN plugin API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api/#search-model) or [Query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/index/) queries. You can refine the results by using a [k-NN search filter]({{site.url}}{{site.baseurl}}/search-plugins/knn/filter-search-knn/).
`query_text` | String | The query text from which to generate text embeddings.
`model_id` | String | The ID of the model that will be used to generate text embeddings from the query text. The model must be indexed in OpenSearch before it can be used in neural search.
`k` | Integer | The number of results returned by the k-NN search.
The following example request uses a Boolean query to combine a filter clause and two query clauses---a neural query and a `match` query. The `script_score` query assigns custom weights to the query clauses:
First, create a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/) with a [`neural_query_enricher`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/neural-query-enricher/) request processor. To set a default model for an index, provide the model ID in the `default_model_id` parameter. To set a default model for a specific field, provide the field name and the corresponding model ID in the `neural_field_default_id` map. If you provide both `default_model_id` and `neural_field_default_id`, `neural_field_default_id` takes precedence:
