ES|QL dense vector functions
Serverless Stack
ES|QL supports dense vector functions for vector similarity calculations and
k-nearest neighbor search.
Dsense vector functions work with
dense_vector fields
and require appropriate field mappings.
ES|QL supports these vector functions:
-
Stack
Serverless
KNN -
Stack
Serverless
TEXT_EMBEDDING
Serverless Stack
Syntax
Parameters
field- Field that the query will target. knn function can be used with dense_vector or semantic_text fields. Other text fields are not allowed
query- Vector value to find top nearest neighbours for.
options-
(Optional) kNN additional options as function named parameters. See knn query for more information.
Description
Finds the k nearest vectors to a query vector, as measured by a similarity metric. knn function finds nearest vectors through approximate search on indexed dense_vectors or semantic_text fields.
Supported types
| field | query | options | result |
|---|---|---|---|
| dense_vector | dense_vector | named parameters | boolean |
| text | dense_vector | named parameters | boolean |
Supported function named parameters
boost- (float) Floating point number used to decrease or increase the relevance scores of the query.Defaults to 1.0.
min_candidates- (integer) The minimum number of nearest neighbor candidates to consider per shard while doing knn search. KNN may use a higher number of candidates in case the query can't use a approximate results. Cannot exceed 10,000. Increasing min_candidates tends to improve the accuracy of the final results. Defaults to 1.5 * LIMIT used for the query.
rescore_oversample- (double) Applies the specified oversampling for rescoring quantized vectors. See oversampling and rescoring quantized vectors for details.
similarity-
(double) 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.
Example
from colors metadata _score
| where knn(rgb_vector, [0, 120, 0])
| sort _score desc, color asc
| color:text | rgb_vector:dense_vector |
|---|---|
| green | [0.0, 128.0, 0.0] |
| black | [0.0, 0.0, 0.0] |
| olive | [128.0, 128.0, 0.0] |
| teal | [0.0, 128.0, 128.0] |
| lime | [0.0, 255.0, 0.0] |
| sienna | [160.0, 82.0, 45.0] |
| maroon | [128.0, 0.0, 0.0] |
| navy | [0.0, 0.0, 128.0] |
| gray | [128.0, 128.0, 128.0] |
| chartreuse | [127.0, 255.0, 0.0] |
Serverless Stack
Syntax
Parameters
text- Text string to generate embeddings from. Must be a non-null literal string value.
inference_id-
Identifier of an existing inference endpoint the that will generate the embeddings. The inference endpoint must have the
text_embeddingtask type and should use the same model that was used to embed your indexed data.
Description
Generates dense vector embeddings from text input using a specified inference endpoint. Use this function to generate query vectors for KNN searches against your vectorized data or others dense vector based operations.
Supported types
| text | inference_id | result |
|---|---|---|
| keyword | keyword | dense_vector |
Examples
Basic text embedding generation from a text string using an inference endpoint.
ROW input="Who is Victor Hugo?"
| EVAL embedding = TEXT_EMBEDDING("Who is Victor Hugo?", "test_dense_inference")
Generate text embeddings and store them in a variable for reuse in KNN vector search queries.
FROM semantic_text METADATA _score
| EVAL query_embedding = TEXT_EMBEDDING("be excellent to each other", "test_dense_inference")
| WHERE KNN(semantic_text_dense_field, query_embedding)
Directly embed text within a KNN query for streamlined vector search without intermediate variables.
FROM semantic_text METADATA _score
| WHERE KNN(semantic_text_dense_field, TEXT_EMBEDDING("be excellent to each other", "test_dense_inference"))