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Support fuzzy matching for apply_diff
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@@ -1,6 +1,59 @@
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import { DiffStrategy } from "../types"
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function levenshteinDistance(a: string, b: string): number {
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const matrix: number[][] = [];
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// Initialize matrix
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for (let i = 0; i <= a.length; i++) {
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matrix[i] = [i];
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}
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for (let j = 0; j <= b.length; j++) {
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matrix[0][j] = j;
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}
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// Fill matrix
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for (let i = 1; i <= a.length; i++) {
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for (let j = 1; j <= b.length; j++) {
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if (a[i-1] === b[j-1]) {
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matrix[i][j] = matrix[i-1][j-1];
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} else {
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matrix[i][j] = Math.min(
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matrix[i-1][j-1] + 1, // substitution
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matrix[i][j-1] + 1, // insertion
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matrix[i-1][j] + 1 // deletion
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);
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}
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}
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}
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return matrix[a.length][b.length];
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}
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function getSimilarity(original: string, search: string): number {
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// Normalize strings by removing extra whitespace but preserve case
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const normalizeStr = (str: string) => str.replace(/\s+/g, ' ').trim();
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const normalizedOriginal = normalizeStr(original);
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const normalizedSearch = normalizeStr(search);
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if (normalizedOriginal === normalizedSearch) { return 1; }
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// Calculate Levenshtein distance
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const distance = levenshteinDistance(normalizedOriginal, normalizedSearch);
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// Calculate similarity ratio (0 to 1, where 1 is exact match)
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const maxLength = Math.max(normalizedOriginal.length, normalizedSearch.length);
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return 1 - (distance / maxLength);
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}
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export class SearchReplaceDiffStrategy implements DiffStrategy {
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private fuzzyThreshold: number;
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constructor(fuzzyThreshold?: number) {
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// Default to exact matching (1.0) unless fuzzy threshold specified
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this.fuzzyThreshold = fuzzyThreshold ?? 1.0;
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}
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getToolDescription(cwd: string): string {
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return `## apply_diff
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Description: Request to replace existing code using search and replace blocks.
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@@ -78,30 +131,24 @@ Your search/replace content here
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const replaceLines = replaceContent.split(/\r?\n/);
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const originalLines = originalContent.split(/\r?\n/);
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// Find the search content in the original
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// Find the search content in the original using fuzzy matching
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let matchIndex = -1;
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let bestMatchScore = 0;
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for (let i = 0; i <= originalLines.length - searchLines.length; i++) {
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let found = true;
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// Join the lines and calculate overall similarity
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const originalChunk = originalLines.slice(i, i + searchLines.length).join('\n');
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const searchChunk = searchLines.join('\n');
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for (let j = 0; j < searchLines.length; j++) {
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const originalLine = originalLines[i + j];
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const searchLine = searchLines[j];
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// Compare lines after removing leading/trailing whitespace
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if (originalLine.trim() !== searchLine.trim()) {
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found = false;
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break;
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}
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}
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if (found) {
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const similarity = getSimilarity(originalChunk, searchChunk);
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if (similarity > bestMatchScore) {
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bestMatchScore = similarity;
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matchIndex = i;
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break;
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}
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}
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if (matchIndex === -1) {
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// Require similarity to meet threshold
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if (matchIndex === -1 || bestMatchScore < this.fuzzyThreshold) {
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return false;
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}
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@@ -121,7 +168,7 @@ Your search/replace content here
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});
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// Apply the replacement while preserving exact indentation
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const indentedReplace = replaceLines.map((line, i) => {
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const indentedReplaceLines = replaceLines.map((line, i) => {
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// Get the corresponding original and search indentations
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const originalIndent = originalIndents[Math.min(i, originalIndents.length - 1)];
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const searchIndent = searchIndents[Math.min(i, searchIndents.length - 1)];
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@@ -162,6 +209,6 @@ Your search/replace content here
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const beforeMatch = originalLines.slice(0, matchIndex);
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const afterMatch = originalLines.slice(matchIndex + searchLines.length);
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return [...beforeMatch, ...indentedReplace, ...afterMatch].join(lineEnding);
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return [...beforeMatch, ...indentedReplaceLines, ...afterMatch].join(lineEnding);
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}
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}
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