anchor.diff:  {
     "query": {
       "script_score": {
  -      "query": {
  -        "match_all": {}
  -      },
  -      "script": {
  -        "source": "cosineSimilarity(params.query_vector, doc['my_dense_vector']) + 1.0", <1>
  -        "params": {
  -          "query_vector": [4, 3.4, -0.2]  <2>
  -        }
  -      }
  -    }
  -  }
  -}
  ---------------------------------------------------
  -// CONSOLE
  -<1> The script adds 1.0 to the cosine similarity to prevent the score from being negative.
  -<2> To take advantage of the script optimizations, provide a query vector as a script parameter.
  +      "query" : {
  +        "bool" : {
  +          "filter" : {
  +            "term" : {
  +              "status" : "published" <1>
  +            }
  +          }
  +        }
  +      },
  +      "script": {
  +        "source": "cosineSimilarity(params.query_vector, doc['my_dense_vector']) + 1.0", <2>
  +        "params": {
  +          "query_vector": [4, 3.4, -0.2]  <3>
  +        }
  +      }
  +    }
  +  }
  +}
  +--------------------------------------------------
  +// CONSOLE
  +<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.
  
positive.code: PUT my_index/_doc/1
  {
    "my_dense_vector": [0.5, 10, 6],
    "my_sparse_vector": {"2": 1.5, "15" : 2, "50": -1.1, "4545": 1.1}
  }
  
  PUT my_index/_doc/2
  {
    "my_dense_vector": [-0.5, 10, 10],
    "my_sparse_vector": {"2": 2.5, "10" : 1.3, "55": -2.3, "113": 1.6}
  }
  
  --------------------------------------------------
  
negative.code: {
    "query": {
      "script_score": {
        "query" : {
          "bool" : {
            "filter" : {
              "term" : {
                "status" : "published"
              }
            }
          }
        },
        "script": {
          "source": "1 / (1 + l1normSparse(params.queryVector, doc['my_sparse_vector']))",
  