Elastic Search - Alternative option of shingle token filter

I would like to implement auto suggest functionality just like google search. Shingle Token Filter is best suitable option for my requirement. Let me explain my index details.

1. Index size is 16gb - 20 million documents. Each document average size is 0.53 kb.

2. Shingle filter is applied on WorkDescripton field. Average size of WorkDescripton field is 0.45kb.

3. Below Analyzer, filter and mapping used at Index creation time

   a. Filter

    "shingle-filter" : {
          "max_shingle_size" : "3",
          "min_shingle_size" : "2",
          "output_unigrams" : "false",
          "type" : "shingle"
        }   
   

   b. Analyzer-

     "ana_autocomplete" : {
          "filter" : [
            "lowercase",
            "shingle-filter"
          ],
          "tokenizer" : "standard"
        }
   

   c. Mapping with work description field is

     "workDesc" : 
     {
        "type" : "text",
        "fields" : 
        {
           "suggestions" : 
           {
              "type" : "text",
              "analyzer" : "ana_autocomplete",
              "fielddata" : true,
              "fielddata_frequency_filter" : 
               {
                 "min" : 0.001,
                 "max" : 0.1,
                 "min_segment_size" : 500
               }
            },
            "workDesc" : 
            {
              "type" : "text",
              "analyzer" : "ana_tenderinfo"
            }
        }
    }
   

4. I used aggregate query to get the result

    GET tenderinfo_version_9/_search
    {
       "aggs":
       {
           "workDesc_111":
           {
              "terms":
              {
                 "field":"workDesc.suggestions",
                  "include":"civil.*",
                 "order":[
                 {
                    "_count":"desc"
                 }],
                 "size":10
              }
           }
       }
       ,"size":0,
       "_source":false
   }
   

5. Auto complete functionality work like charm (result coming in millisecond) when documents are 0.1 million, But I got time out error when documents are 20 million.

6. Elastic search server configuration is

   Operation System - Ubantu 18.04.6 LTS

   CPU - Intel(R) Core(TM) i5-7400 CPU @ 3.00 GHz. 1 physical processor, 4 core, 4 threads

   RAM - 16gb

Is there any other options to get the same result? Please advise