fix: uncapped fuzzy sort return and sort by "score"

Author: rhit-daughewd

fred/fred_commands/_command_utils.py

@@ -1,30 +1,54 @@
 from typing import Type
 
-from regex import ENHANCEMATCH, match, escape
+from regex import ENHANCEMATCH, match, escape, search as re_search
 
 from ..config import Commands, Crashes, Misc
 from ..libraries.common import new_logger
 
 logger = new_logger("[Command/Crash Search]")
 
 
-def search(table: Type[Commands | Crashes], pattern: str, column: str, force_fuzzy: bool) -> (str | list[str], bool):
-    """Returns the top three results based on the result"""
+def search(table: Type[Commands | Crashes], pattern: str, column: str, force_fuzzy: bool) -> tuple[str | list[str], bool]:
+    """Returns the top results based on the result.
+
+    This function performs an exact lookup unless `force_fuzzy` is True or no
+    exact match is found. Fuzzy results are returned as a list of names (best
+    matches first). The score is computed from the regex Match.fuzzy_counts()
+    (inserts + deletes + substitutions) and used only for sorting/filtering.
+    """
 
     if column not in dir(table):
         raise KeyError(f"`{column}` is not a column in the {table.__name__} table!")
 
     if not force_fuzzy and (exact_match := table.fetch_by(column, pattern)):
         return exact_match[column], True
 
-    fuzzy_pattern = rf".*(?:{escape(pattern)}){{e<={min(len(pattern) // 3, 6)}}}.*"
-    fuzzies: list[str] = [
-        item["name"]
-        for item in table.fetch_all()
-        if (item.get(column, None) is not None) and match(fuzzy_pattern, item[column], flags=ENHANCEMATCH)
-    ]
-    logger.info(fuzzies)
-    return fuzzies[:5], False
+    # Set fuzzy range - (1/3 pattern length, max 6)
+    max_edits = min(len(pattern) // 3, 6)
+    substring_pattern = rf".*(?:{escape(pattern)}){{e<={max_edits}}}.*"
+
+    scored_results: list[tuple[int, str]] = []
+    for item in table.fetch_all():
+        value = item.get(column)
+        
+        # Filter non matching strings
+        if not isinstance(value, str):
+            continue
+        if not re_search(substring_pattern, value, flags=ENHANCEMATCH):
+            continue
+
+        # add levenshtein score
+        score = levenshtein(pattern, value)
+        scored_results.append((score, item["name"]))
+
+    # Sort by score, then alphabetically
+    scored_results.sort(key=lambda x: (x[0], x[1]))
+    results = [name for _, name in scored_results]
+
+    # Return all results fitting fuzzy range
+    logger.info(results)
+    return results, False
+
 
 
 def get_search(table: Type[Commands | Crashes], pattern: str, column: str, force_fuzzy: bool) -> str:
@@ -54,3 +78,25 @@ def get_search(table: Type[Commands | Crashes], pattern: str, column: str, force
         response = e.args[0]
 
     return response
+
+
+# Levenshtein distance algorithm
+def levenshtein(a: str, b: str) -> int:
+        if a == b:
+            return 0
+        la, lb = len(a), len(b)
+        if la == 0:
+            return lb
+        if lb == 0:
+            return la
+
+        prev = list(range(lb + 1))
+        for i, ca in enumerate(a, start=1):
+            cur = [i] + [0] * lb
+            for j, cb in enumerate(b, start=1):
+                ins = cur[j - 1] + 1
+                delete = prev[j] + 1
+                sub = prev[j - 1] + (0 if ca == cb else 1)
+                cur[j] = min(ins, delete, sub)
+            prev = cur
+        return prev[lb]