* 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>
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 %}