docs/reference/query-languages/query-dsl/query-dsl-knn-query.md
Finds the k nearest vectors to a query vector, as measured by a similarity metric. knn query finds nearest vectors through approximate search on indexed dense_vectors or semantic_text fields which use dense_vector under the hood. For dense_vector fields, you can also use the top level knn section of a search request.
::::{note}
The top-level knn option does not support semantic_text fields. To run a kNN search on a semantic_text field, use the knn query described on this page. You can also query semantic_text fields using a match query, which is the simplest approach.
::::
PUT my-image-index
{
"mappings": {
"properties": {
"image-vector": {
"type": "dense_vector",
"dims": 3,
"index": true,
"similarity": "l2_norm"
},
"file-type": {
"type": "keyword"
},
"title": {
"type": "text"
}
}
}
}
Index your data.
POST my-image-index/_bulk?refresh=true
{ "index": { "_id": "1" } }
{ "image-vector": [1, 5, -20], "file-type": "jpg", "title": "mountain lake" }
{ "index": { "_id": "2" } }
{ "image-vector": [42, 8, -15], "file-type": "png", "title": "frozen lake"}
{ "index": { "_id": "3" } }
{ "image-vector": [15, 11, 23], "file-type": "jpg", "title": "mountain lake lodge" }
% TEST[continued]
Run the search using the knn query, asking for the top 10 nearest vectors from each shard, and then combine shard results to get the top 3 global results.
POST my-image-index/_search
{
"size" : 3,
"query" : {
"knn": {
"field": "image-vector",
"query_vector": [-5, 9, -12],
"k": 10
}
}
}
% TEST[continued]
{applies_to}stack: ga 9.0-9.3 You can also provide a hex-encoded query vector string. Hex query vectors are byte-oriented (one byte per dimension, represented as two hex characters). For example, [-5, 9, -12] as signed bytes is fb09f4.
POST my-image-index/_search
{
"size" : 3,
"query" : {
"knn": {
"field": "image-vector",
"query_vector": "fb09f4",
"k": 10
}
}
}
% TEST[continued]
{applies_to}stack: ga 9.4 You can also provide a base64-encoded query vector string. For example, [-5, 9, -12] encoded as float32 big-endian bytes is wKAAAEEQAADBQAAA.
POST my-image-index/_search
{
"size" : 3,
"query" : {
"knn": {
"field": "image-vector",
"query_vector": "wKAAAEEQAADBQAAA",
"k": 10
}
}
}
% TEST[continued]
knn [knn-query-top-level-parameters]field
: (Required, string) The name of the vector field to search against. Must be a dense_vector field with indexing enabled, or a semantic_text field with a compatible dense vector {{infer}} model.
$$$knn-query-query-vector$$$ query_vector
: (Optional, array of floats or string) Query vector. Must have the same number of dimensions as the vector field you are searching against.
Must be one of:
- An array of floats
- A hex-encoded byte vector (one byte per dimension; for bit, one byte per 8 dimensions). {applies_to}stack: ga 9.0-9.3
- A base64-encoded vector string. Base64 supports float and bfloat16 (big-endian), byte, and bit encodings depending on the target field type. {applies_to}stack: ga 9.4 {applies_to}serverless: ga
Either this or query_vector_builder must be provided.
$$$knn-query-query-vector-builder$$$ query_vector_builder
: (Optional, object) Query vector builder. A configuration object indicating how to build a query vector before executing the request. You must provide either a query_vector_builder or query_vector, but not both. Refer to Query vector builder types for parameter details and Query vector builder examples for usage examples.
k
: (Optional, integer) The number of nearest neighbors to return from each shard. {{es}} collects k (or k * oversample if conditions for rescore_vector are met) results from each shard, then merges them to find the global top k results. This value must be less than or equal to num_candidates. Defaults to search request size.
num_candidates
: (Optional, integer) The number of nearest neighbor candidates to consider per shard while doing knn search. Cannot exceed 10,000. Increasing num_candidates tends to improve the accuracy of the final results. Defaults to 1.5 * k if k is set, or 1.5 * size if k is not set. When rescore_vector are met) is applied, num_candidates is set to max(num_candidates, k * oversample)
visit_percentage {applies_to}stack: ga 9.2
: (Optional, float) The percentage of vectors to explore per shard while doing knn search with bbq_disk. Must be between 0 and 100. 0 will default to using num_candidates for calculating the percent visited. Increasing visit_percentage tends to improve the accuracy of the final results. If visit_percentage is set for bbq_disk, num_candidates is ignored. Defaults to ~1% per shard for every 1 million vectors.
filter
: (Optional, query object) Query to filter the documents that can match. The kNN search will return the top documents that also match this filter. The value can be a single query or a list of queries. If filter is not provided, all documents are allowed to match.
The filter is a pre-filter, meaning that it is applied during the approximate kNN search to ensure that num_candidates matching documents are returned.
similarity
: (Optional, float) The minimum similarity required for a document to be considered a match. The similarity value calculated relates to the raw similarity used. Not the document score. The matched documents are then scored according to similarity and the provided boost is applied.
boost
: (Optional, float) Floating point number used to multiply the scores of matched documents. This value cannot be negative. Defaults to 1.0.
_name
: (Optional, string) Name field to identify the query
rescore_vector {applies_to}stack: preview =9.0, ga 9.1+
: (Optional, object) Apply oversampling and rescoring to quantized vectors.
**Parameters for `rescore_vector`**:
`oversample`
: (Required, float)
Applies the specified oversample factor to `k` on the approximate kNN search. The approximate kNN search will:
* Retrieve `num_candidates` candidates per shard.
* From these candidates, the top `k * oversample` candidates per shard will be rescored using the original vectors.
* The top `k` rescored candidates will be returned. Must be one of the following values:
* \>= 1f to indicate the oversample factor
* Exactly `0` to indicate that no oversampling and rescoring should occur. {applies_to}`stack: ga 9.1`
For more information, refer to [oversampling and rescoring quantized vectors](docs-content://solutions/search/vector/knn.md#dense-vector-knn-search-rescoring).
::::{note}
Rescoring only makes sense for [quantized](/reference/elasticsearch/mapping-reference/dense-vector.md#dense-vector-quantization) vectors. The `rescore_vector` option will be ignored for non-quantized `dense_vector` fields, because the original vectors are used for scoring.
::::
There are two ways to filter documents that match a kNN query:
k matching documents are returned.Pre-filtering is supported through the filter parameter of the knn query. Also filters from aliases are applied as pre-filters.
All other filters found in the Query DSL tree are applied as post-filters. For example, knn query finds the top 3 documents with the nearest vectors (k=3), which are combined with term filter, that is post-filtered. The final set of documents will contain only a single document that passes the post-filter.
POST my-image-index/_search
{
"size" : 10,
"query" : {
"bool" : {
"must" : {
"knn": {
"field": "image-vector",
"query_vector": [-5, 9, -12],
"k": 3
}
},
"filter" : {
"term" : { "file-type" : "png" }
}
}
}
}
% TEST[continued]
Knn query can be used as a part of hybrid search, where knn query is combined with other lexical queries. For example, the query below finds documents with title matching mountain lake, and combines them with the top 10 documents that have the closest image vectors to the query_vector. The combined documents are then scored and the top 3 top scored documents are returned.
POST my-image-index/_search
{
"size" : 3,
"query": {
"bool": {
"should": [
{
"match": {
"title": {
"query": "mountain lake",
"boost": 1
}
}
},
{
"knn": {
"field": "image-vector",
"query_vector": [-5, 9, -12],
"k": 10,
"boost": 2
}
}
]
}
}
}
% TEST[continued]
The knn query can be used inside a nested query. The behaviour here is similar to top level nested kNN search:
dense_vectors diversifies the top results over the top-level documentfilter both over the top-level document metadata and nested is supported and acts as a pre-filterTo ensure correct results: each individual filter must be either over:
nested metadata {applies_to}stack: ga 9.2
:::{note}
A single knn query supports multiple filters, where some filters can be over the top-level metadata and some over nested.
:::This query performs a basic nested knn search:
{
"query" : {
"nested" : {
"path" : "paragraph",
"query" : {
"knn": {
"query_vector": [0.45, 0.50],
"field": "paragraph.vector"
}
}
}
}
}
% NOTCONSOLE
stack: ga 9.2
This query filters over nested metadata. For scoring parent documents, this query only considers vectors that have "paragraph.language" set to "EN":
{
"query" : {
"nested" : {
"path" : "paragraph",
"query" : {
"knn": {
"query_vector": [0.45, 0.50],
"field": "paragraph.vector",
"filter": {
"match": {
"paragraph.language": "EN"
}
}
}
}
}
}
}
% NOTCONSOLE
stack: ga 9.2
This query uses multiple filters: one over nested metadata and another over the top level metadata. For scoring parent documents, this query only considers vectors whose parent's title contain "essay" word and have "paragraph.language" set to "EN":
{
"query" : {
"nested" : {
"path" : "paragraph",
"query" : {
"knn": {
"query_vector": [0.45, 0.50],
"field": "paragraph.vector",
"filter": [
{
"match": {
"paragraph.language": "EN"
}
},
{
"match": {
"title": "essay"
}
}
]
}
}
}
}
}
% NOTCONSOLE
Note that nested knn only supports score_mode=max.
::::{note}
The top-level knn search option does not support semantic_text fields. Use the knn query shown below to run a kNN search on a semantic_text field which uses dense_vector under the hood. For simpler use cases, you can also use a match query directly on the semantic_text field.
::::
Elasticsearch supports knn queries over a
semantic_text field.
Here is an example using the query_vector_builder:
{
"query": {
"knn": {
"field": "inference_field",
"k": 10,
"num_candidates": 100,
"query_vector_builder": {
"text_embedding": {
"model_text": "test"
}
}
}
}
}
% NOTCONSOLE
Note that for semantic_text fields, the model_id does not have to be
provided as it can be inferred from the semantic_text field mapping.
Knn search using query vectors over semantic_text fields is also supported,
with no change to the API.
Query vector builders let you generate vectors directly from inputs such as text or base64-encoded images at search time.
{{es}} provides three query vector builders. Each builder generates a query vector from a different type of input or source.
text_embedding: Generates a query vector from text input. This is useful when your application sends raw text, such as a search query, and you want {{es}} to convert it into an embedding automatically instead of generating the vector in advance.embedding: {applies_to}stack: preview 9.4 {applies_to}serverless: preview Generates a query vector from multimodal input, such as text or base64-encoded images. Use this when you want to generate embeddings dynamically from different types of input without creating them in advance.lookup: {applies_to}stack: ga 9.4 Retrieves an existing vector from a stored document to use as the query vector. This is useful when you want to find documents similar to an existing document, without generating a new embedding at search time.Refer to Query vector builder types for parameter details and Query vector builder examples for usage examples.
$$$knn-query-builder-lookup$$$ lookup {applies_to}stack: ga 9.4
: Build the query vector by looking up an existing document's vector. For an example, refer to lookup.
**Parameters for `lookup`**:
`id`
: (Required, string) The ID of the document to look up.
`path`
: (Required, string) The name of the vector field in the document to use as the query vector.
`index`
: (Required, string) The name of the index containing the document to look up
`routing`
: (Optional, string) The routing value to use when looking up the document.
$$$knn-query-builder-text-embedding$$$ text_embedding
: Build the query vector by generating an embedding from input text. For an example, refer to text_embedding.
**Parameters for `text_embedding`**:
`model_id`
: (Optional, string) Identifier of the text embedding model that generates the query vector. Use the same model that produced vectors in your index.
::::{note}
When you query only semantic_text fields, you can omit model_id because {{es}} uses the inference_id from the semantic_text field mapping (for example the search-time {{infer}} endpoint configured on the field).
For dense_vector fields or when you need a different model than the one mapped on semantic_text, set model_id explicitly.
::::
`model_text`
: (Required, string) The query text passed to the model to produce the embedding.
For an example request, refer to [`text_embedding`](#text-embedding-builder). For a broader overview of semantic kNN search, refer to [Perform semantic search](docs-content://solutions/search/vector/knn.md#knn-semantic-search).
$$$knn-query-builder-embedding$$$ embedding {applies_to}stack: preview 9.4 {applies_to}serverless: preview
: Build the query vector by generating an embedding from text or a base64-encoded image. This enables multimodal search, where different types of input can be used to generate a vector and retrieve similar documents. For an example, refer to embedding.
**Parameters for `embedding`**:
`inference_id`
: (Required, string) The ID of the {{infer}} service used to generate the embedding. Must reference an {{infer}} service configured with the `embedding` task type.
`input`
: (Required, string, object, or array) The input used to generate the query vector. You can provide the input in one of the following formats:
- Single object: A single multimodal input (text or image). For an example, refer to [single input object](#embedding-example-single-object).
- Array of objects: Multiple inputs. These are combined into a single embedding. For an example, refer to [multiple inputs](#embedding-example-array-input).
- String: A single text input. Equivalent to `value` when providing an object with "type": "text". For an example, refer to [string input](#embedding-example-string-input).
::::{dropdown} Parameters for input (when input is an object or an array of objects)
:open:
type
: (Required, string) The type of the input. For text input, use text. For image input, use image.
format
: (Optional, string) The format of the input value. If omitted, the default for the type is used. For text input, specify text or omit (defaults to text). For image input, specify base64 or omit (defaults to base64).
value
: (Required, string) The value to generate the embedding from. For text input, a text string. For image input, must be a data URI for a base64-encoded image.
::::
`timeout`
: (Optional, time value) Maximum time to wait for the embedding {{infer}} request to complete. Defaults to `30s` when omitted.
lookup [lookup-builder]stack: ga 9.4
Use the lookup query vector builder to retrieve an existing vector from a stored document and use it as the query vector.
{
"knn": {
"field": "dense-vector-field",
"k": 10,
"num_candidates": 100,
"query_vector_builder": {
"lookup": {
"index": "my-index", <1>
"id": "document-1", <2>
"path": "my_vector" <3>
}
}
}
}
text_embedding [text-embedding-builder]Use the text_embedding query vector builder to generate a query vector from text input.
POST my-index/_search
{
"knn": {
"field": "dense-vector-field",
"k": 10,
"num_candidates": 100,
"query_vector_builder": {
"text_embedding": {
"model_id": "my-text-embedding-model", <1>
"model_text": "The opposite of blue" <2>
}
}
}
}
semantic_text fields, you can omit model_id because {{es}} uses the inference_id from the semantic_text field mapping (for example the search-time {{infer}} endpoint configured on the field).embedding [embedding-builder]stack: preview 9.4
serverless: preview
Use the embedding query vector builder to generate a query vector from multimodal input.
This builder supports both text and base64-encoded image inputs. You can also use multiple inputs to generate a query vector, enabling multimodal search scenarios such as searching with both text and an image.
POST my-index/_search
{
"knn": {
"field": "dense-vector-field",
"k": 10,
"num_candidates": 100,
"query_vector_builder": {
"embedding": {
"inference_id": "my-embedding-endpoint", <1>
"input": {
"type": "image", <2>
"format": "base64", <3>
"value": "data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAAUA\nAAAAFCAIAAAACDbGyAAAAHElEQVQI12P4\n//8/w38GIAXDIBKE0DHxgljNBAAO\n9TXL0Y4OHwAAAABJRU5ErkJggg==" <4>
}
}
}
}
}
EMBEDDING task type.text and image.text for text input. Defaults to base64 for image input.POST my-index/_search
{
"knn": {
"field": "dense-vector-field",
"k": 10,
"num_candidates": 100,
"query_vector_builder": {
"embedding": {
"inference_id": "my-embedding-endpoint", <1>
"input": [
{
"type": "text",
"value": "red shoes"
},
{
"type": "image",
"format": "base64", <2>
"value": "data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAAUA\nAAAAFCAIAAAACDbGyAAAAHElEQVQI12P4\n//8/w38GIAXDIBKE0DHxgljNBAAO\n9TXL0Y4OHwAAAABJRU5ErkJggg==" <3>
}
]
}
}
}
}
EMBEDDING task type.base64 for image input.POST my-index/_search
{
"knn": {
"field": "dense-vector-field",
"k": 10,
"num_candidates": 100,
"query_vector_builder": {
"embedding": {
"inference_id": "my-embedding-endpoint", <1>
"input": "red shoes" <2>
}
}
}
}
EMBEDDING task type.{
"type": "text",
"value": "red shoes"
}
knn query calculates aggregations on top k documents from each shard. Thus, the final results from aggregations contain k * number_of_shards documents. This is different from the top level knn section where aggregations are calculated on the global top k nearest documents.