kolchfa-aws a97c719591
Add multimodal search/sparse search/pre- and post-processing function documentation (#5168)
* Add multimodal search documentation

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Text image embedding processor

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Add prerequisite

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Change query text

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Added bedrock connector tutorial and renamed ML TOC

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Name changes and rewording

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Change connector link

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Change link

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Implemented tech review comments

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Link fix and field name fix

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Add default text embedding preprocessing and post-processing functions

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Add sparse search documentation

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Fix links

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Pre/post processing function tech review comments

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Fix link

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Sparse search tech review comments

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Apply suggestions from code review

Co-authored-by: Melissa Vagi <vagimeli@amazon.com>
Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>

* Implemented doc review comments

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Add actual test sparse pipeline response

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Added tested examples

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Added model choice for sparse search

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Remove Bedrock connector

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Implemented tech review feedback

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Add that the model must be deployed to neural search

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Apply suggestions from code review

Co-authored-by: Nathan Bower <nbower@amazon.com>
Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>

* Link fix

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Add session token to sagemaker blueprint

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Formatted bullet points the same way

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Specified both model types in neural sparse query

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Added more explanation for default pre/post-processing functions

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Remove framework and extensibility references

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Minor rewording

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

---------

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>
Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>
Co-authored-by: Melissa Vagi <vagimeli@amazon.com>
Co-authored-by: Nathan Bower <nbower@amazon.com>
2023-10-16 10:45:35 -04:00

2.0 KiB

layout title parent grand_parent nav_order
default Neural Specialized queries Query DSL 50

Neural query

Use the neural query for vector field search in neural search.

Request fields

Include the following request fields in the neural query:

"neural": {
  "<vector_field>": {
    "query_text": "<query_text>",
    "query_image": "<image_binary>",
    "model_id": "<model_id>",
    "k": 100
  }
}

The top-level vector_field specifies the vector field against which to run a search query. The following table lists the other neural query fields.

Field | Data type | Required/Optional | Description :--- | :--- | :--- query_text | String | Optional | The query text from which to generate vector embeddings. You must specify at least one query_text or query_image. query_image | String | Optional | A base-64 encoded string that corresponds to the query image from which to generate vector embeddings. You must specify at least one query_text or query_image. model_id | String | Required if the default model ID is not set. For more information, see Setting a default model on an index or field. | The ID of the model that will be used to generate vector embeddings from the query text. 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. k | Integer | Optional | The number of results returned by the k-NN search. Default is 10.

Example request

GET /my-nlp-index/_search
{
  "query": {
    "neural": {
      "passage_embedding": {
        "query_text": "Hi world",
        "query_image": "iVBORw0KGgoAAAAN...",
        "k": 100
      }
    }
  }
}

{% include copy-curl.html %}