Elasticsearch Edge-NGrams Prefer Shorter Terms - elasticsearch

Question

I like the results I am getting from Elasticsearch using Edge-NGrams to index data and a different analyzer for searching. I would, however, prefer that shorter terms that match get ranked higher than longer terms.
For example, take the terms ABC100 and ABC100xxx. If I perform a query using the term ABC, I get back both of these documents as hits with the same score. What I would like is for ABC100 to be scored higher than ABC100xxx because ABC closer matches ABC100 according to something like the Levenshtein distance algorithm.
Setting up the index:
PUT stackoverflow
{
"settings": {
"index": {
"number_of_replicas": 0,
"number_of_shards": 1
},
"analysis": {
"filter": {
"edge_ngram": {
"type": "edgeNGram",
"min_gram": "1",
"max_gram": "20"
}
},
"analyzer": {
"my_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"edge_ngram"
]
}
}
}
},
"mappings": {
"doc": {
"properties": {
"product": {
"type": "text",
"analyzer": "my_analyzer",
"search_analyzer": "whitespace"
}
}
}
}
}
Inserting documents:
PUT stackoverflow/doc/1
{
"product": "ABC100"
}
PUT stackoverflow/doc/2
{
"product": "ABC100xxx"
}
Search query:
GET stackoverflow/_search?pretty
{
"query": {
"match": {
"product": "ABC"
}
}
}
Results:
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.28247002,
"hits": [
{
"_index": "stackoverflow",
"_type": "doc",
"_id": "2",
"_score": 0.28247002,
"_source": {
"product": "ABC100xxx"
}
},
{
"_index": "stackoverflow",
"_type": "doc",
"_id": "1",
"_score": 0.28247002,
"_source": {
"product": "ABC100"
}
}
]
}
}
Does anyone know how I may have a shorter term such as ABC100 ranked higher than ABC100xxx?

Answer 1

After finding plenty of less than optimal solutions regarding storing field length as a field or using a script query, I found the root of my problem. It was simply because I was using the edge_ngrams token filter instead of the the edge_ngrams tokenizer.