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399b80c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 | """Fuzzy matching chain for SEARCH/REPLACE edits.
The chain degrades gracefully:
Level 1 β exact match
Level 2 β line-trimmed match (per-line strip)
Level 3 β block-anchor match (first/last line + Levenshtein middle)
Level 4 β whitespace-normalized match (collapse whitespace)
Level 5 β indentation-flexible match (strip common indent)
Level 6 β trimmed-boundary match (strip entire block)
"""
from __future__ import annotations
import re
from typing import Generator, List, Optional, Tuple
from openspace.utils.logging import Logger
logger = Logger.get_logger(__name__)
__all__ = [
"fuzzy_find_match",
"fuzzy_replace",
"REPLACER_CHAIN",
]
# Type alias β each replacer yields candidate match strings.
Replacer = Generator[str, None, None]
# Thresholds
SINGLE_CANDIDATE_SIMILARITY_THRESHOLD = 0.0
MULTIPLE_CANDIDATES_SIMILARITY_THRESHOLD = 0.3
def levenshtein(a: str, b: str) -> int:
"""Compute the Levenshtein edit distance between two strings."""
if not a or not b:
return max(len(a), len(b))
rows = len(a) + 1
cols = len(b) + 1
matrix = [[0] * cols for _ in range(rows)]
for i in range(rows):
matrix[i][0] = i
for j in range(cols):
matrix[0][j] = j
for i in range(1, rows):
for j in range(1, cols):
cost = 0 if a[i - 1] == b[j - 1] else 1
matrix[i][j] = min(
matrix[i - 1][j] + 1,
matrix[i][j - 1] + 1,
matrix[i - 1][j - 1] + cost,
)
return matrix[len(a)][len(b)]
def simple_replacer(_content: str, find: str) -> Replacer:
"""Yield *find* unconditionally; the caller verifies via ``str.find``."""
yield find
def line_trimmed_replacer(content: str, find: str) -> Replacer:
"""Match by trimming each line, then yield the original substring."""
original_lines = content.split("\n")
search_lines = find.split("\n")
# Strip trailing empty line (common LLM artifact)
if search_lines and search_lines[-1] == "":
search_lines.pop()
if not search_lines:
return
n_search = len(search_lines)
for i in range(len(original_lines) - n_search + 1):
matches = True
for j in range(n_search):
if original_lines[i + j].strip() != search_lines[j].strip():
matches = False
break
if matches:
start_idx = sum(len(original_lines[k]) + 1 for k in range(i))
end_idx = start_idx
for k in range(n_search):
end_idx += len(original_lines[i + k])
if k < n_search - 1:
end_idx += 1
yield content[start_idx:end_idx]
def block_anchor_replacer(content: str, find: str) -> Replacer:
"""Anchor on first/last lines (trimmed) and use Levenshtein on middles."""
original_lines = content.split("\n")
search_lines = find.split("\n")
if len(search_lines) < 3:
return
if search_lines and search_lines[-1] == "":
search_lines.pop()
if len(search_lines) < 3:
return
first_search = search_lines[0].strip()
last_search = search_lines[-1].strip()
search_block_size = len(search_lines)
candidates: List[Tuple[int, int]] = []
for i, line in enumerate(original_lines):
if line.strip() != first_search:
continue
for j in range(i + 2, len(original_lines)):
if original_lines[j].strip() == last_search:
candidates.append((i, j))
break
if not candidates:
return
def _extract_block(start_line: int, end_line: int) -> str:
s = sum(len(original_lines[k]) + 1 for k in range(start_line))
e = s
for k in range(start_line, end_line + 1):
e += len(original_lines[k])
if k < end_line:
e += 1
return content[s:e]
if len(candidates) == 1:
start_line, end_line = candidates[0]
actual_size = end_line - start_line + 1
lines_to_check = min(search_block_size - 2, actual_size - 2)
if lines_to_check > 0:
similarity = 0.0
for j in range(1, min(search_block_size - 1, actual_size - 1)):
orig_line = original_lines[start_line + j].strip()
srch_line = search_lines[j].strip()
max_len = max(len(orig_line), len(srch_line))
if max_len == 0:
continue
dist = levenshtein(orig_line, srch_line)
similarity += (1 - dist / max_len) / lines_to_check
if similarity >= SINGLE_CANDIDATE_SIMILARITY_THRESHOLD:
break
else:
similarity = 1.0
if similarity >= SINGLE_CANDIDATE_SIMILARITY_THRESHOLD:
yield _extract_block(start_line, end_line)
return
# Multiple candidates: pick the best
best_match: Optional[Tuple[int, int]] = None
max_similarity = -1.0
for start_line, end_line in candidates:
actual_size = end_line - start_line + 1
lines_to_check = min(search_block_size - 2, actual_size - 2)
if lines_to_check > 0:
raw_sim = 0.0
for j in range(1, min(search_block_size - 1, actual_size - 1)):
orig_line = original_lines[start_line + j].strip()
srch_line = search_lines[j].strip()
max_len = max(len(orig_line), len(srch_line))
if max_len == 0:
continue
dist = levenshtein(orig_line, srch_line)
raw_sim += 1 - dist / max_len
similarity = raw_sim / lines_to_check
else:
similarity = 1.0
if similarity > max_similarity:
max_similarity = similarity
best_match = (start_line, end_line)
if max_similarity >= MULTIPLE_CANDIDATES_SIMILARITY_THRESHOLD and best_match:
yield _extract_block(best_match[0], best_match[1])
def whitespace_normalized_replacer(content: str, find: str) -> Replacer:
r"""Normalize whitespace (``\s+`` -> single space) before comparing."""
def _normalize(text: str) -> str:
return re.sub(r"\s+", " ", text).strip()
normalized_find = _normalize(find)
lines = content.split("\n")
# Single-line matching
for line in lines:
if _normalize(line) == normalized_find:
yield line
else:
normalized_line = _normalize(line)
if normalized_find in normalized_line:
words = find.strip().split()
if words:
pattern = r"\s+".join(re.escape(word) for word in words)
try:
match = re.search(pattern, line)
if match:
yield match.group(0)
except re.error:
pass
# Multi-line matching
find_lines = find.split("\n")
if len(find_lines) > 1:
for i in range(len(lines) - len(find_lines) + 1):
block = lines[i: i + len(find_lines)]
if _normalize("\n".join(block)) == normalized_find:
yield "\n".join(block)
def indentation_flexible_replacer(content: str, find: str) -> Replacer:
"""Remove the common leading indentation and compare blocks."""
def _remove_indent(text: str) -> str:
lines = text.split("\n")
non_empty = [line for line in lines if line.strip()]
if not non_empty:
return text
min_indent = min(len(line) - len(line.lstrip()) for line in non_empty)
return "\n".join(
line[min_indent:] if line.strip() else line for line in lines
)
normalized_find = _remove_indent(find)
content_lines = content.split("\n")
find_lines = find.split("\n")
for i in range(len(content_lines) - len(find_lines) + 1):
block = "\n".join(content_lines[i: i + len(find_lines)])
if _remove_indent(block) == normalized_find:
yield block
def trimmed_boundary_replacer(content: str, find: str) -> Replacer:
"""Trim the entire find block, then search."""
trimmed_find = find.strip()
if trimmed_find == find:
return
if trimmed_find in content:
yield trimmed_find
lines = content.split("\n")
find_lines = find.split("\n")
for i in range(len(lines) - len(find_lines) + 1):
block = "\n".join(lines[i: i + len(find_lines)])
if block.strip() == trimmed_find:
yield block
REPLACER_CHAIN: list = [
("simple", simple_replacer),
("line_trimmed", line_trimmed_replacer),
("block_anchor", block_anchor_replacer),
("whitespace_normalized", whitespace_normalized_replacer),
("indentation_flexible", indentation_flexible_replacer),
("trimmed_boundary", trimmed_boundary_replacer),
]
def fuzzy_find_match(content: str, find: str) -> Tuple[str, int]:
"""Locate *find* in *content* using the replacer chain.
Returns ``(matched_text, position)`` where *matched_text* is the
actual substring of *content*, and *position* is its character offset.
Returns ``("", -1)`` when no match is found.
"""
for name, replacer in REPLACER_CHAIN:
for candidate in replacer(content, find):
pos = content.find(candidate)
if pos == -1:
continue
if name != "simple":
logger.debug(
"fuzzy_find_match: matched via '%s' at position %d",
name, pos,
)
return candidate, pos
return "", -1
def fuzzy_replace(
content: str,
old_string: str,
new_string: str,
replace_all: bool = False,
) -> str:
"""Replace *old_string* with *new_string* in *content*.
Walks the chain until a unique match is found.
Raises:
ValueError: When old_string not found or match is ambiguous.
"""
if old_string == new_string:
raise ValueError("old_string and new_string are identical")
not_found = True
for name, replacer in REPLACER_CHAIN:
for candidate in replacer(content, old_string):
idx = content.find(candidate)
if idx == -1:
continue
not_found = False
if replace_all:
return content.replace(candidate, new_string)
last_idx = content.rfind(candidate)
if idx != last_idx:
continue # ambiguous
return content[:idx] + new_string + content[idx + len(candidate):]
if not_found:
raise ValueError(
"Could not find old_string in the file. "
"Must match exactly (including whitespace and indentation)."
)
raise ValueError(
"Found multiple matches for old_string. "
"Provide more context to make the match unique."
)
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