Normalizing knn and multi_match clauses

Elastic Stack Elasticsearch
1

I have a product catalog as Elasticsearch (8.16.1) index.
Each doc represent a product with different fields, such as name, color, etc.
I've added a vector as additional field to each product / doc.

My search is can be a free text, such as "red dress".
I am transforming this free text to vector and find the closest products with knn query which works great and return relevant products with 0 < _score < 1.0.

I am also querying with multi_match that works great but provide _score with unknown range (somewhere between 0 < _score < 25.0, but I can't point what the highest value is).

I want to combine those two approaches together, with an equal weight (or kind of control boost value) between knn and multi_match.

Since the knn has a lower _score range (top 1.0) - the impact of its result is negligible and the multi_match impact too much.

Here is my query, how can I change it / improve it to get the the same contribution for the knn and the multi_match?

{
  "bool": {
    "should": [
      {
        "knn": {
          "field": "embedding_vector",
          "query_vector": [
            1,
            2,
            3
          ],
          "num_candidates": 10000,
          "filter": [
            {
              "term": {
                "categories.keyword": "Dresses"
              }
            }
          ]
        }
      },
      {
        "multi_match": {
          "query": "red dress",
          "fields": [
            "categories",
            "name",
            "color"
          ],
          "fuzziness": "AUTO",
          "type": "best_fields"
        }
      }
    ]
  }
}

Thanks!