public Task SearchAsync()

in src/WebJobs.Extensions.OpenAI.Kusto/KustoSearchProvider.cs [97:156]
50 lines of code
6 McCabe index (conditional complexity)

    public Task<SearchResponse> SearchAsync(SearchRequest request)
    {
        (ICslQueryProvider kustoQueryClient, KustoConnectionStringBuilder connectionStringBuilder) =
            this.kustoQueryClients.GetOrAdd(
                request.ConnectionInfo.ConnectionName,
                name =>
                {
                    KustoConnectionStringBuilder connectionStringBuilder = this.GetKustoConnectionString(name);
                    ICslQueryProvider client = KustoClientFactory.CreateCslQueryProvider(connectionStringBuilder);
                    return (client, connectionStringBuilder);
                });

        // TODO: Use query parameters to remove the possibility of KQL-injection:
        // https://learn.microsoft.com/azure/data-explorer/kusto/query/queryparametersstatement
        // NOTE: Vector similarity reference:
        // https://techcommunity.microsoft.com/t5/azure-data-explorer-blog/azure-data-explorer-for-vector-similarity-search/ba-p/3819626
        string embeddingsList = GetEmbeddingsString(request.Embeddings, false);
        string? tableName = request.ConnectionInfo.CollectionName?.Trim();
        if (string.IsNullOrEmpty(tableName) ||
            tableName.Contains('/') ||
            tableName.Contains(';') ||
            tableName.Any(char.IsWhiteSpace))
        {
            throw new InvalidOperationException($"The table name '{tableName}' is invalid.");
        }

        string query = $$"""
            let series_cosine_similarity_fl=(vec1:dynamic, vec2:dynamic, vec1_size:real=double(null), vec2_size:real=double(null))
            {
                let dp = series_dot_product(vec1, vec2);
                let v1l = iff(isnull(vec1_size), sqrt(series_dot_product(vec1, vec1)), vec1_size);
                let v2l = iff(isnull(vec2_size), sqrt(series_dot_product(vec2, vec2)), vec2_size);
                dp/(v1l*v2l)
            };
            let search_vector=pack_array({{embeddingsList}});
            {{tableName}}
            | extend similarity = series_cosine_similarity_fl(search_vector, Embeddings)
            | top {{request.MaxResults}} by similarity desc
            | project similarity, Id, Title, Text, Embeddings, Timestamp
            """;

        this.logger.LogDebug("Executing Kusto query: {query}", query);
        using IDataReader reader = kustoQueryClient.ExecuteQuery(
            databaseName: connectionStringBuilder.InitialCatalog,
            query,
            new ClientRequestProperties());

        List<SearchResult> results = new(capacity: request.MaxResults);
        while (reader.Read())
        {
            string subject = (string)reader["Title"];
            string text = (string)reader["Text"];
            results.Add(new SearchResult(subject, text));
        }

        this.logger.LogDebug("Kusto similarity query returned {count} results", results.Count);

        SearchResponse response = new(results);
        return Task.FromResult(response);
    }