I am trying to get my autocomplete search the best as possible. So I started with the completion suggester functions where I can define some weights on my documents suggest fields. This allows me to give some pre ranking while indexing my documents.
Berlin -> weight = 10 Bern -> weight = 8
If I search for
Ber I will get this two documents where
Berlin is higher ranked. Thats fine for now.
But lets say I have another document like
Hotel Berlin Alexanderplatz. If I search for
Ber now the Hotel will not appear. So I splitted the words by space and created some suggestions. If I search again for
Ber the hotel will be in the response. Good for now but a problem ahead. If I search for
Berlin Hotel the hotel will not be in the response anymore, because there is no suggestion for
So I searched for a better solution and found out that
edge_ngram tokenizer does what I need. It breaks down text into words. Which will make
Hotel Berlin Alexanderplatz findable in all combinations. Thats nice but I miss my weights now. Becuase these are very important for the result.
So is there a solution to use the weight feature of
complete suggester and the word break down of an
edge_ngram tokenizer together?