How to use completion suggester and edge NGram Tokenizer function together?

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.

For example:

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 Berling Hotel.

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?

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