134 lines
5.3 KiB
Markdown
134 lines
5.3 KiB
Markdown
---
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layout: default
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title: Multimodal search
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nav_order: 40
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has_children: false
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redirect_from:
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- /search-plugins/neural-multimodal-search/
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---
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# Multimodal search
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Introduced 2.11
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{: .label .label-purple }
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Use multimodal search to search text and image data. In neural search, text search is facilitated by multimodal embedding models.
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**PREREQUISITE**<br>
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Before using text search, you must set up a multimodal embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
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{: .note}
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## Using multimodal search
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To use neural search with text and image embeddings, follow these steps:
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1. [Create an ingest pipeline](#step-1-create-an-ingest-pipeline).
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1. [Create an index for ingestion](#step-2-create-an-index-for-ingestion).
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1. [Ingest documents into the index](#step-3-ingest-documents-into-the-index).
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1. [Search the index using neural search](#step-4-search-the-index-using-neural-search).
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## Step 1: Create an ingest pipeline
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To generate vector embeddings, you need to create an [ingest pipeline]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/) that contains a [`text_image_embedding` processor]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/processors/text-image-embedding/), which will convert the text or image in a document field to vector embeddings. The processor's `field_map` determines the text and image fields from which to generate vector embeddings and the output vector field in which to store the embeddings.
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The following example request creates an ingest pipeline where the text from `image_description` and an image from `image_binary` will be converted into text embeddings and the embeddings will be stored in `vector_embedding`:
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```json
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PUT /_ingest/pipeline/nlp-ingest-pipeline
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{
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"description": "A text/image embedding pipeline",
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"processors": [
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{
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"text_image_embedding": {
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"model_id": "-fYQAosBQkdnhhBsK593",
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"embedding": "vector_embedding",
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"field_map": {
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"text": "image_description",
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"image": "image_binary"
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}
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}
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}
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]
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}
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```
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{% include copy-curl.html %}
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## Step 2: Create an index for ingestion
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In order to use the text embedding processor defined in your pipeline, create a k-NN index, adding the pipeline created in the previous step as the default pipeline. Ensure that the fields defined in the `field_map` are mapped as correct types. Continuing with the example, the `vector_embedding` field must be mapped as a k-NN vector with a dimension that matches the model dimension. Similarly, the `image_description` field should be mapped as `text`, and the `image_binary` should be mapped as `binary`.
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The following example request creates a k-NN index that is set up with a default ingest pipeline:
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```json
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PUT /my-nlp-index
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{
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"settings": {
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"index.knn": true,
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"default_pipeline": "nlp-ingest-pipeline",
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"number_of_shards": 2
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},
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"mappings": {
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"properties": {
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"vector_embedding": {
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"type": "knn_vector",
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"dimension": 1024,
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"method": {
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"name": "hnsw",
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"engine": "lucene",
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"parameters": {}
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}
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},
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"image_description": {
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"type": "text"
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},
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"image_binary": {
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"type": "binary"
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}
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}
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}
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}
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```
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{% include copy-curl.html %}
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For more information about creating a k-NN index and its supported methods, see [k-NN index]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index/).
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## Step 3: Ingest documents into the index
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To ingest documents into the index created in the previous step, send the following request:
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```json
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PUT /nlp-index/_doc/1
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{
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"image_description": "Orange table",
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"image_binary": "iVBORw0KGgoAAAANSUI..."
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}
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```
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{% include copy-curl.html %}
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Before the document is ingested into the index, the ingest pipeline runs the `text_image_embedding` processor on the document, generating vector embeddings for the `image_description` and `image_binary` fields. In addition to the original `image_description` and `image_binary` fields, the indexed document includes the `vector_embedding` field, which contains the combined vector embeddings.
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## Step 4: Search the index using neural search
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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/). You can search by text, image, or both text and image.
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The following example request uses a neural query to search for text and image:
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```json
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GET /my-nlp-index/_search
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{
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"size": 10,
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"query": {
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"neural": {
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"vector_embedding": {
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"query_text": "Orange table",
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"query_image": "iVBORw0KGgoAAAANSUI...",
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"model_id": "-fYQAosBQkdnhhBsK593",
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"k": 5
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}
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}
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}
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}
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```
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{% include copy-curl.html %}
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To eliminate passing the model ID with each neural query request, you can set a default model on a k-NN index or a field. To learn more, see [Setting a default model on an index or field]({{site.url}}{{site.baseurl}}/search-plugins/neural-text-search/##setting-a-default-model-on-an-index-or-field).
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