Dense vector query

Performs exact, brute-force scoring of a query vector against every document that has a value for a dense_vector field. Unlike the approximate knn query, it does not use an index structure or a k/num_candidates cutoff: every matching document is scored. This is useful when you need exact scores, want to combine exact vector scoring with other queries, or want to score a dense_vector field that is not indexed for approximate search (index: false).

By default, scoring uses the original, full-precision vectors, so a quantized index still produces full-precision scores. See quantized to score against the quantized representation instead.

				PUT my-image-index
					{
  "mappings": {
    "properties": {
      "image-vector": {
        "type": "dense_vector",
        "dims": 3,
        "index": true,
        "similarity": "l2_norm"
      }
    }
  }
}
		

1. Index your data.

   				POST my-image-index/_bulk?refresh=true
   					{ "index": { "_id": "1" } }
   { "image-vector": [1, 5, -20] }
   { "index": { "_id": "2" } }
   { "image-vector": [42, 8, -15] }
   { "index": { "_id": "3" } }
   { "image-vector": [15, 11, 23] }
   		

2. Run the dense_vector query. Every document with the field is scored.

   				POST my-image-index/_search
   					{
     "query": {
       "dense_vector": {
         "field": "image-vector",
         "query_vector": [-5, 9, -12]
       }
     }
   }
   		

field

(Required, string) The name of the dense_vector field to search against.

query_vector

(Optional, array of floats or string) The query vector. Must have the same number of dimensions as the target field. You must provide either query_vector or query_vector_builder, but not both. Accepts a float array, or a hex-encoded or base64-encoded string.

query_vector_builder

(Optional, object) A configuration object used to convert a query into a query_vector, for example by running a text embedding model. You must provide either query_vector or query_vector_builder, but not both. See query_vector_builder for details.

similarity_function

(Optional, string) The similarity metric used for scoring, overriding the field's mapped similarity for this query. One of l2_norm, dot_product, cosine, or max_inner_product. Defaults to the field's configured similarity, or to cosine for a non-indexed (index: false) field, which has no configured similarity. For bit fields, only l2_norm is supported. Cannot be combined with quantized: true.

quantized [dense-vector-query-quantized]

(Optional, Boolean) Defaults to false.

When false (the default), scoring iterates the preserved full-precision vectors, producing raw scores regardless of the field's index_options.

When true, scoring uses the codec's scorer, which on a quantized index scores against the lossy quantized representation (faster, lower fidelity). It cannot be combined with similarity_function.

Note

quantized: true only makes sense for quantized fields. For non-quantized dense_vector fields, the setting is ignored and the original vectors are used for scoring.

boost

(Optional, float) Floating point number used to multiply the scores of the query. Defaults to 1.0.

_name

(Optional, string) Name to identify the query for named queries.

The dense_vector query can score a field mapped with index: false, whose vectors are stored as doc values rather than in an approximate-search index. A non-indexed field has no configured similarity, so scoring uses cosine unless you specify a different metric with similarity_function:

				POST my-image-index/_search
					{
  "query": {
    "dense_vector": {
      "field": "image-vector",
      "query_vector": [-5, 9, -12],
      "similarity_function": "cosine"
    }
  }
}