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/**
* Context-aware matcher that uses surrounding lines as "anchors"
* to disambiguate between identical or similar blocks.
*/
export class ContextAwareMatcher implements ContentMatcher {
readonly name = 'ContextAwareMatcher';
/**
* Confidence threshold for accepting a context-aware match.
*/
private readonly confidenceThreshold: number = parseFloat(
process.env.CONTEXT_AWARE_MATCH_THRESHOLD || '0.85',
);
/**
* Number of lines to consider as context on each side.
*/
private readonly anchorWindow: number = parseInt(
process.env.CONTEXT_ANCHOR_WINDOW || '3',
);
/**
* Calculate optimal anchor window based on file characteristics.
* Dense files need more context to find unique anchors.
*/
private getAdaptiveAnchorWindow(contentLines: string[]): number {
const totalLines = contentLines.length;
const totalChars = contentLines.join('\n').length;
// Handle edge cases
if (totalLines === 0 || totalChars === 0) {
return 3; // Default window for empty/tiny files
}
// Calculate density (lines per 1000 characters)
const density = (totalLines / totalChars) * 1000;
// Dense files (many short lines) need more lines for uniqueness
if (density > 50) return 5; // Very dense (minified, JSON)
if (density > 25) return 4; // Dense (short lines)
return 3; // Normal/sparse (typical code)
}
match(searchBlock: string, content: string): MatchResult {
const searchLines = searchBlock.split('\n');
const contentLines = content.split('\n');
// Use adaptive window based on file density
const adaptiveWindow = this.getAdaptiveAnchorWindow(contentLines);
// Find all candidates with similarity scoring
const candidates = this.findCandidatesWithScoring(
searchBlock,
searchLines,
contentLines,
adaptiveWindow,
);
if (candidates.length === 0) {
return {
found: false,
unique: false,
confidence: 0,
candidates: [],
};
}
// Filter by confidence threshold
const highConfidenceCandidates = candidates.filter(
(c) => c.confidence >= this.confidenceThreshold,
);
Iif (highConfidenceCandidates.length === 0) {
return {
found: true,
unique: false,
confidence: Math.max(...candidates.map((c) => c.confidence)),
candidates,
};
}
// Check uniqueness
if (highConfidenceCandidates.length === 1) {
const candidate = highConfidenceCandidates[0];
const before = this.extractBefore(candidate, contentLines);
const after = this.extractAfter(candidate, contentLines);
return {
found: true,
unique: true,
confidence: candidate.confidence,
before,
after,
};
}
// Multiple high-confidence matches - check if one is significantly better
// This handles disambiguation when one block has unique context
highConfidenceCandidates.sort((a, b) => b.confidence - a.confidence);
const best = highConfidenceCandidates[0];
const secondBest = highConfidenceCandidates[1];
// Only consider it unique if the gap is very large (>= 25%)
// This prevents treating similar contexts as unique
Iif (best.confidence - secondBest.confidence >= 0.25) {
const before = this.extractBefore(best, contentLines);
const after = this.extractAfter(best, contentLines);
return {
found: true,
unique: true,
confidence: best.confidence,
before,
after,
};
}
// Multiple high-confidence matches
return {
found: true,
unique: false,
confidence: Math.max(
...highConfidenceCandidates.map((c) => c.confidence),
),
candidates: highConfidenceCandidates,
};
}
/**
* Find candidates and score them based on:
* 1. Similarity of the block itself
* 2. Uniqueness of surrounding context (anchors)
*/
private findCandidatesWithScoring(
searchBlock: string,
searchLines: string[],
contentLines: string[],
windowSize: number,
): MatchCandidate[] {
const candidates: MatchCandidate[] = [];
for (let i = 0; i <= contentLines.length - searchLines.length; i++) {
const windowLines = contentLines.slice(i, i + searchLines.length);
const windowBlock = windowLines.join('\n');
const blockSimilarity = this.calculateSimilarity(
searchBlock,
windowBlock,
);
// Only consider blocks with reasonable similarity
if (blockSimilarity < 0.5) {
continue;
}
// Calculate anchor uniqueness scores
const contextScore = this.calculateContextUniqueness(
i,
searchLines.length,
contentLines,
windowSize,
);
// Combine block similarity with context uniqueness
// If anchors are unique (score > 0.5), give them higher weight
// This helps disambiguate identical blocks with unique context
let combinedConfidence: number;
if (contextScore > 0.5) {
// Unique context - boost confidence significantly
combinedConfidence = blockSimilarity * 0.4 + contextScore * 0.6;
} else E{
// Common context - rely more on block similarity
combinedConfidence = blockSimilarity * 0.9 + contextScore * 0.1;
}
const beforeAnchor = i > 0 ? contentLines[i - 1] : undefined;
const afterAnchor =
i + searchLines.length < contentLines.length
? contentLines[i + searchLines.length]
: undefined;
candidates.push({
block: windowBlock,
startLine: i,
endLine: i + searchLines.length - 1,
confidence: combinedConfidence,
beforeAnchor,
afterAnchor,
});
}
// Sort by combined confidence descending
return candidates.sort((a, b) => b.confidence - a.confidence);
}
/**
* Calculate how unique the context around a position is.
* Higher score means the context is more distinctive (appears fewer times in the file).
*/
private calculateContextUniqueness(
startIndex: number,
blockLength: number,
contentLines: string[],
windowSize: number,
): number {
const totalLines = contentLines.length;
// Extract before-anchor context (window of lines before the block)
const beforeStart = Math.max(0, startIndex - windowSize);
const beforeLines = contentLines.slice(beforeStart, startIndex);
// Extract after-anchor context (window of lines after the block)
const afterEnd = Math.min(
totalLines,
startIndex + blockLength + windowSize,
);
const afterLines = contentLines.slice(startIndex + blockLength, afterEnd);
// Count occurrences of before context
let beforeOccurrences = 0;
// Empty before context means start of file - treat as unique
if (beforeLines.length > 0) {
for (let i = 0; i <= totalLines - beforeLines.length; i++) {
const window = contentLines.slice(i, i + beforeLines.length);
if (this.arraysEqual(window, beforeLines)) {
beforeOccurrences++;
}
}
} else {
beforeOccurrences = 0; // Start of file is unique
}
// Count occurrences of after context
let afterOccurrences = 0;
// Empty after context means end of file - treat as unique
if (afterLines.length > 0) {
for (let i = 0; i <= totalLines - afterLines.length; i++) {
const window = contentLines.slice(i, i + afterLines.length);
if (this.arraysEqual(window, afterLines)) {
afterOccurrences++;
}
}
} else {
afterOccurrences = 0; // End of file is unique
}
// Calculate uniqueness score (1.0 = completely unique)
const beforeUniqueness =
beforeOccurrences === 0 ? 1.0 : 1.0 / beforeOccurrences;
const afterUniqueness =
afterOccurrences === 0 ? 1.0 : 1.0 / afterOccurrences;
// Average the two
return (beforeUniqueness + afterUniqueness) / 2;
}
/**
* Helper to compare string arrays for equality.
*/
private arraysEqual(a: string[], b: string[]): boolean {
Iif (a.length !== b.length) return false;
for (let i = 0; i < a.length; i++) {
if (a[i] !== b[i]) return false;
}
return true;
}
/**
* Calculate similarity using Levenshtein distance.
*/
private calculateSimilarity(a: string, b: string): number {
if (a === b) return 1.0;
if (a.length === 0) return 0.0;
if (b.length === 0) return 0.0;
const distance = this.levenshteinDistance(a, b);
const maxLen = Math.max(a.length, b.length);
return 1.0 - distance / maxLen;
}
/**
* Calculate Levenshtein distance.
*/
private levenshteinDistance(a: string, b: string): number {
const matrix: number[][] = [];
for (let i = 0; i <= b.length; i++) {
matrix[i] = [i];
}
for (let j = 0; j <= a.length; j++) {
matrix[0][j] = j;
}
for (let i = 1; i <= b.length; i++) {
for (let j = 1; j <= a.length; j++) {
const cost = b[i - 1] === a[j - 1] ? 0 : 1;
matrix[i][j] = Math.min(
matrix[i - 1][j] + 1,
matrix[i][j - 1] + 1,
matrix[i - 1][j - 1] + cost,
);
}
}
return matrix[b.length][a.length];
}
private extractBefore(
candidate: MatchCandidate,
contentLines: string[],
): string {
return contentLines.slice(0, candidate.startLine).join('\n');
}
private extractAfter(
candidate: MatchCandidate,
contentLines: string[],
): string {
return contentLines.slice(candidate.endLine + 1).join('\n');
}
}
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