elasticsearch/docs/reference/vectors/vector-functions.asciidoc

[role="xpack"]
[testenv="basic"]
[[vector-functions]]
===== Functions for vector fields

NOTE: During vector functions' calculation, all matched documents are
linearly scanned. Thus, expect the query time grow linearly
with the number of matched documents. For this reason, we recommend
to limit the number of matched documents with a `query` parameter.

Let's create an index with a `dense_vector` mapping and index a couple
of documents into it.

[source,console]
--------------------------------------------------
PUT my-index-000001
{
  "mappings": {
    "properties": {
      "my_dense_vector": {
        "type": "dense_vector",
        "dims": 3
      },
      "status" : {
        "type" : "keyword"
      }
    }
  }
}

PUT my-index-000001/_doc/1
{
  "my_dense_vector": [0.5, 10, 6],
  "status" : "published"
}

PUT my-index-000001/_doc/2
{
  "my_dense_vector": [-0.5, 10, 10],
  "status" : "published"
}

POST my-index-000001/_refresh

--------------------------------------------------
// TESTSETUP

The `cosineSimilarity` function calculates the measure of
cosine similarity between a given query vector and document vectors.

[source,console]
--------------------------------------------------
GET my-index-000001/_search
{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published" <1>
            }
          }
        }
      },
      "script": {
        "source": "cosineSimilarity(params.query_vector, 'my_dense_vector') + 1.0", <2>
        "params": {
          "query_vector": [4, 3.4, -0.2]  <3>
        }
      }
    }
  }
}
--------------------------------------------------

<1> To restrict the number of documents on which script score calculation is applied, provide a filter.
<2> The script adds 1.0 to the cosine similarity to prevent the score from being negative.
<3> To take advantage of the script optimizations, provide a query vector as a script parameter.

NOTE: If a document's dense vector field has a number of dimensions
different from the query's vector, an error will be thrown.

The `dotProduct` function calculates the measure of
dot product between a given query vector and document vectors.

[source,console]
--------------------------------------------------
GET my-index-000001/_search
{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published"
            }
          }
        }
      },
      "script": {
        "source": """
          double value = dotProduct(params.query_vector, 'my_dense_vector');
          return sigmoid(1, Math.E, -value); <1>
        """,
        "params": {
          "query_vector": [4, 3.4, -0.2]
        }
      }
    }
  }
}
--------------------------------------------------

<1> Using the standard sigmoid function prevents scores from being negative.

The `l1norm` function calculates L^1^ distance
(Manhattan distance) between a given query vector and
document vectors.

[source,console]
--------------------------------------------------
GET my-index-000001/_search
{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published"
            }
          }
        }
      },
      "script": {
        "source": "1 / (1 + l1norm(params.queryVector, 'my_dense_vector'))", <1>
        "params": {
          "queryVector": [4, 3.4, -0.2]
        }
      }
    }
  }
}
--------------------------------------------------

<1> Unlike `cosineSimilarity` that represent similarity, `l1norm` and
`l2norm` shown below represent distances or differences. This means, that
the more similar the vectors are, the lower the scores will be that are
produced by the `l1norm` and `l2norm` functions.
Thus, as we need more similar vectors to score higher,
we reversed the output from `l1norm` and `l2norm`. Also, to avoid
division by 0 when a document vector matches the query exactly,
we added `1` in the denominator.

The `l2norm` function calculates L^2^ distance
(Euclidean distance) between a given query vector and
document vectors.

[source,console]
--------------------------------------------------
GET my-index-000001/_search
{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published"
            }
          }
        }
      },
      "script": {
        "source": "1 / (1 + l2norm(params.queryVector, 'my_dense_vector'))",
        "params": {
          "queryVector": [4, 3.4, -0.2]
        }
      }
    }
  }
}
--------------------------------------------------

NOTE: If a document doesn't have a value for a vector field on which
a vector function is executed, an error will be thrown.

You can check if a document has a value for the field `my_vector` by
`doc['my_vector'].size() == 0`. Your overall script can look like this:

[source,js]
--------------------------------------------------
"source": "doc['my_vector'].size() == 0 ? 0 : cosineSimilarity(params.queryVector, 'my_vector')"
--------------------------------------------------
// NOTCONSOLE