Use distance on dense vectors in relevance score (at query time)

Elastic Stack Elasticsearch
2

Hi there!
Did I understand your intention correctly: you want to go through all documents, and apply cosine similarity function to them. Then you also have a query and for the documents that match a query, you want to calculate score for this query. Then you want to combine these two scores: from a cosine similarity and a query?
Currently, you can do that with a bool query using should clauses like this:

GET my_index/_search
{
  "query": {
      "bool": {
        "should" : [
          {
            "match": {
                "my_text": {
                    "query": "abc"
                }
            }
          },
          {
            "script_score" : {
              "query" : {"match_all" : {}},
              "script" : {
                "source": "50 * cosineSimilarity(params.query_vector, doc['my_vector']) + 1.0",
                "params": {
                    "query_vector": [0, 0, 1]
                }
              }
            }
          }
        ]
      }  
  }
}

This will give you a sum of scores: score1 + score2. You can also apply boost for any query.
We also have a plan to develop a compound query that will give you an option to combine scores of queries not only through sum option. But this is not available yet.