Users fuzzy search enhancements

[Petitoto]

Description

This PR improves users fuzzy search results.

Searching algorithm is refactored in a more simple way:

• for each user:
  • attribute a score based on the query, the user attributes and a similarity algorithm
  • insert the score and its corresponding user in a sorted list
  • remove the lowest score of the list if its size is superior to the limit parameter (thus, always keeping the list sorted with the N=limit best results)

The score of each user correspond to the highest Jaro-Winkler similarity between the query and:

• firstname
• name
• firstname + name
• name + firstname
• nickname (if exists)

This method aims to fit real searches: queries are often related to one of these 5 strings, but we don't know which one. The higher the similarity is between the query and one of them, the more likely the query is related to it. For well-constructed queries, false positives will always come after the good results.

Before running the Jaro-Winkler algorithm, all strings are "unaccentuated" to make the similarity algorithm insensitive to accents. Moreover, queries from the /users/search endpoint are "capworded". We assume that queries from this endpoint are often the beginning of a name / firstname / nickname. This way, queries like max will better match Maxou than Kmax, which may better corresponds to the enduser's search.

This method has proven to give better results on limited subsets of users (~30) and queries (~20), while keeping one of the highest performance. Other methods tested include:

• previous sort_user() function
• SequenceMatcher of the standard DiffLib library
• Jaro-Winkler algorithm from RapidFuzz library (however, on longer strings, RapidFuzz pretend to be faster than Jellyfish)
• Indel algorithm from RapidFuzz library
• Damerau-Levenshtein algorithm from RapidFuzz library
• partial_ratio() from RapidFuzz library
• token_ratio() from RapidFuzz library
• partial_token_ratio() from RapidFuzz library

On the tested data with a limit parameter of 10, the new function introduced by this PR takes 50% more time than the old one.
This is due to more similarities beeing computed (with 4 instead of 5, both take approximately the same time).
However, it still seems to be reasonable for production use, and is better than other tested algorithms, especially when list or number of users in the database increase.

Checklist

• Created tests which fail without the change (if possible)
• All tests passing
• Extended the documentation, if necessary

[codecov]

Codecov Report

Attention: Patch coverage is 94.11765% with 1 line in your changes missing coverage. Please review.

Project coverage is 81.83%. Comparing base (91302a3) to head (57f861a).
Report is 37 commits behind head on main.

Files with missing lines	Patch %	Lines
app/utils/tools.py	93.33%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #541      +/-   ##
==========================================
+ Coverage   81.77%   81.83%   +0.06%     
==========================================
  Files         125      125              
  Lines        9485     9504      +19     
==========================================
+ Hits         7756     7778      +22     
+ Misses       1729     1726       -3     

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[armanddidierjean]

This is brillant!