Points refactor

[jmarin]

Improves the address points search by doing a match search instead of match_phrase in Elasticsearch. This produces more positive results through a fuzzier search, but the relevance (and thus spatial accuracy) could be lower. In order to provide a sense of quality, the result provides a match score that detects how similar the input and found strings are (maximum value 1 or 100% equal)

[hkeeler]

@jmarin, overall looks good. I think it would be worthwhile to add some comments for SearchUtils. levenshtein as to why this is necessary, even though Elasticsearch's fuzziness feature appears to do the same thing.

One main difference I see is that Elasticsearch's fuzziness feature only supports a max of 2 edits per string, and we'd definitely need more for how you're proposing we use this.

For reference: Elasticsearch Fuzziness

[jmarin]

@hkeeler The fuzziness feature in Elasticsearch is different from the Levenshtein implementation as used in this PR. While the algorithm is the same, the ES one is something you pass to the query, as a parameter or configuration to affect fuzziness of a term search (or match, as per the documentation, and the one we are using). The way this is being used here is for reporting the difference between what was found by the engine vs what was issued as input (in %, derived from the Levenshtein distance).