ElasticsearchとGoを使用して、ホリネズミ狩りをハイブリッド検索

func GetElasticsearchClient() (*elasticsearch.TypedClient, error) {
	var cloudID = os.Getenv("ELASTIC_CLOUD_ID")
	var apiKey = os.Getenv("ELASTIC_API_KEY")

	var es, err = elasticsearch.NewTypedClient(elasticsearch.Config{
		CloudID: cloudID,
		APIKey:  apiKey,
		Logger:  &elastictransport.ColorLogger{os.Stdout, true, true},
	})

	if err != nil {
		return nil, fmt.Errorf("unable to connect: %w", err)
	}

	return es, nil
}

func HybridSearchWithBoost(client *elasticsearch.TypedClient, term string) ([]Rodent, error) {
	var k = 10
	var numCandidates = 10
	var knnBoost float32 = 0.2
	var queryBoost float32 = 0.8

	res, err := client.Search().
		Index("vector-search-rodents").
		Knn(types.KnnSearch{
			Field:         "text_embedding.predicted_value",
			Boost:         &knnBoost,
			K:             &k,
			NumCandidates: &numCandidates,
			QueryVectorBuilder: &types.QueryVectorBuilder{
				TextEmbedding: &types.TextEmbedding{
					ModelId:   "sentence-transformers__msmarco-minilm-l-12-v3",
					ModelText: term,
				},
			}}).
		Query(&types.Query{
			Match: map[string]types.MatchQuery{
				"title": {
					Query: term,
					Boost: &queryBoost,
				},
			},
		}).
		Do(context.Background())

	if err != nil {
		return nil, err
	}

	return getRodents(res.Hits.Hits)
}

score := 0.0
// q is a query in the set of queries (vector and keyword search)
for _, q := range queries {
    // result(q) is the results 
    if document in result(q) {
        // k is a ranking constant (default 60)
        // rank(result(q), d) is the document's rank within result(q) 
        // range from 1 to the window_size (default 100)
        score +=  1.0 / (k + rank(result(q), d))
    }
}

return score

func HybridSearchWithRRF(client *elasticsearch.TypedClient, term string) ([]Rodent, error) {
	var k = 10
	var numCandidates = 10

	// Minimum required window size for the default result size of 10
	var windowSize int64 = 10
	var rankConstant int64 = 42

	res, err := client.Search().
		Index("vector-search-rodents").
		Knn(types.KnnSearch{
			Field:         "text_embedding.predicted_value",
			K:             &k,
			NumCandidates: &numCandidates,
			QueryVectorBuilder: &types.QueryVectorBuilder{
				TextEmbedding: &types.TextEmbedding{
					ModelId:   "sentence-transformers__msmarco-minilm-l-12-v3",
					ModelText: term,
				},
			}}).
		Query(&types.Query{
			Match: map[string]types.MatchQuery{
				"title": {Query: term},
			},
		}).
		Rank(&types.RankContainer{
			Rrf: &types.RrfRank{
				WindowSize:   &windowSize,
				RankConstant: &rankConstant,
			},
		}).
		Do(context.Background())

	if err != nil {
		return nil, err
	}

	return getRodents(res.Hits.Hits)
}