New vector_rescore parameter as a quantized index type option

[benwtrent]

This adds a new parameter to the quantized index mapping that allows default oversampling and rescoring to occur.

This doesn't adjust any of the defaults. It allows it to be configured. When the user provides rescore_vector: {oversample: <number>} in the query it will overwrite it.

For example, here is how to use it with bbq:

PUT rescored_bbq
{
  "mappings": {
    "properties": {
      "vector": {
        "type": "dense_vector",
        "index_options": {
          "type": "bbq_hnsw",
          "rescore_vector": {"oversample": 3.0}
        }
      }
    }
  }
}

Then, when querying, it will auto oversample the k by 3x and rerank with the raw vectors.

POST _search
{
  "knn": {
    "query_vector": [...],
    "field": "vector"
  }
}

[elasticsearchmachine]

Pinging @elastic/es-search-relevance (Team:Search Relevance)

[elasticsearchmachine]

Hi @benwtrent, I've created a changelog YAML for you.

[carlosdelest]

LGTM. 🎉 Left some small doc comments

[carlosdelest]

Nit - I find hard to follow multiple ternary operators

Suggested change

	Float oversample = indexOptions instanceof QuantizedIndexOptions quantizedIndexOptions
	? quantizedIndexOptions.rescoreVector != null ? quantizedIndexOptions.rescoreVector.oversample() : null
	: null;
	if (queryOversample != null) {
	oversample = queryOversample;
	}
	Float oversample = null;
	if (queryOversample != null) {
	oversample = queryOversample;
	} else if (queryOversample indexOptions instanceof QuantizedIndexOptions quantizedIndexOptions) {
	oversample = quantizedIndexOptions.rescoreVector != null ? quantizedIndexOptions.rescoreVector.oversample() : null
	}

[benwtrent]

I will refactor