mirror of
https://github.com/pacnpal/Roo-Code.git
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refactor: enhance search strategies with adaptive thresholds and overlapping windows
- Introduced adaptive confidence thresholds based on file size to improve search accuracy. - Implemented overlapping window functionality in search strategies to capture matches more effectively. - Added helper functions for evaluating content uniqueness and creating overlapping windows. - Enhanced existing search functions (exact, similarity, and Levenshtein) to utilize new strategies for better match validation. - Improved logging for search results to facilitate debugging and analysis of search performance.
This commit is contained in:
Daniel Riccio
@@ -1,40 +1,71 @@
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import { compareTwoStrings } from 'string-similarity';
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import { closest } from 'fastest-levenshtein';
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import { diff_match_patch } from 'diff-match-patch';
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import { Change, Hunk } from './types';
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import { compareTwoStrings } from "string-similarity"
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import { closest } from "fastest-levenshtein"
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import { diff_match_patch } from "diff-match-patch"
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import { Change, Hunk } from "./types"
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export type SearchResult = {
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index: number;
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confidence: number;
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strategy: string;
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};
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index: number
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confidence: number
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strategy: string
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}
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//TODO: this should be configurable
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const MIN_CONFIDENCE = 0.97;
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const MIN_CONFIDENCE = 0.97
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const MIN_CONFIDENCE_LARGE_FILE = 0.9
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const LARGE_FILE_THRESHOLD = 1000 // lines
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const UNIQUE_CONTENT_BOOST = 0.05
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const DEFAULT_OVERLAP_SIZE = 3 // lines of overlap between windows
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const MAX_WINDOW_SIZE = 500 // maximum lines in a window
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// Helper function to calculate adaptive confidence threshold based on file size
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function getAdaptiveThreshold(contentLength: number): number {
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if (contentLength <= LARGE_FILE_THRESHOLD) {
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return MIN_CONFIDENCE
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}
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return MIN_CONFIDENCE_LARGE_FILE
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}
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// Helper function to evaluate content uniqueness
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function evaluateContentUniqueness(searchStr: string, content: string[]): number {
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const searchLines = searchStr.split("\n")
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const uniqueLines = new Set(searchLines)
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const contentStr = content.join("\n")
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// Calculate how many search lines are relatively unique in the content
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let uniqueCount = 0
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for (const line of uniqueLines) {
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const regex = new RegExp(line.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"), "g")
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const matches = contentStr.match(regex)
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if (matches && matches.length <= 2) {
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// Line appears at most twice
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uniqueCount++
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}
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}
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return uniqueCount / uniqueLines.size
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}
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// Helper function to prepare search string from context
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export function prepareSearchString(changes: Change[]): string {
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const lines = changes
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.filter((c) => c.type === 'context' || c.type === 'remove')
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.map((c) => c.content);
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return lines.join('\n');
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const lines = changes.filter((c) => c.type === "context" || c.type === "remove").map((c) => c.originalLine)
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return lines.join("\n")
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}
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// Helper function to evaluate similarity between two texts
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export function evaluateSimilarity(original: string, modified: string): number {
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return compareTwoStrings(original, modified);
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return compareTwoStrings(original, modified)
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}
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// Helper function to validate using diff-match-patch
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export function getDMPSimilarity(original: string, modified: string): number {
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const dmp = new diff_match_patch();
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const diffs = dmp.diff_main(original, modified);
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dmp.diff_cleanupSemantic(diffs);
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const patches = dmp.patch_make(original, diffs);
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const [expectedText] = dmp.patch_apply(patches, original);
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const dmp = new diff_match_patch()
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const diffs = dmp.diff_main(original, modified)
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dmp.diff_cleanupSemantic(diffs)
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const patches = dmp.patch_make(original, diffs)
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const [expectedText] = dmp.patch_apply(patches, original)
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const similarity = evaluateSimilarity(expectedText, modified);
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return similarity;
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const similarity = evaluateSimilarity(expectedText, modified)
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return similarity
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}
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// Helper function to validate edit results using hunk information
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@@ -43,116 +74,204 @@ export function getDMPSimilarity(original: string, modified: string): number {
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// returns 0.1 (0.5 * (1 - 0.8)) to reduce confidence proportionally but with less impact.
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// If similarity >= MIN_CONFIDENCE, returns 0 (no reduction).
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export function validateEditResult(hunk: Hunk, result: string, strategy: string): number {
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const hunkDeepCopy: Hunk = JSON.parse(JSON.stringify(hunk));
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const hunkDeepCopy: Hunk = JSON.parse(JSON.stringify(hunk))
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// Create skeleton of original content (context + removed lines)
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const originalSkeleton = hunkDeepCopy.changes
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.filter((change) => change.type === 'context' || change.type === 'remove')
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.filter((change) => change.type === "context" || change.type === "remove")
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.map((change) => change.content)
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.join('\n');
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.join("\n")
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// Create skeleton of expected result (context + added lines)
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const expectedSkeleton = hunkDeepCopy.changes
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.filter((change) => change.type === 'context' || change.type === 'add')
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.filter((change) => change.type === "context" || change.type === "add")
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.map((change) => change.content)
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.join('\n');
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.join("\n")
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// Compare with original content
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const originalSimilarity = evaluateSimilarity(originalSkeleton, result);
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console.log('originalSimilarity ', strategy, originalSimilarity);
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const originalSimilarity = evaluateSimilarity(originalSkeleton, result)
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console.log("originalSimilarity ", strategy, originalSimilarity)
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// Compare with expected result
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const expectedSimilarity = evaluateSimilarity(expectedSkeleton, result)
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console.log("expectedSimilarity", strategy, expectedSimilarity)
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console.log("result", result)
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// If original similarity is 1 and expected similarity is not 1, it means changes weren't applied
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if (originalSimilarity > 0.97 && expectedSimilarity !== 1) {
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if (originalSimilarity === 1) {
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// If original similarity is 1, it means changes weren't applied
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if (originalSimilarity > 0.97) {
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if (originalSimilarity === 1) {
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return 0.5; // Significant confidence reduction
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return 0.5 // Significant confidence reduction
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} else {
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return 0.8;
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return 0.8
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}
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}
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} else {
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return 0.8
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}
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}
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// Compare with expected result
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const expectedSimilarity = evaluateSimilarity(expectedSkeleton, result);
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console.log('expectedSimilarity', strategy, expectedSimilarity);
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// Scale between 0.98 and 1.0 (4% impact) based on expected similarity
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const multiplier =
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expectedSimilarity < MIN_CONFIDENCE ? 0.96 + 0.04 * expectedSimilarity : 1;
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const multiplier = expectedSimilarity < MIN_CONFIDENCE ? 0.96 + 0.04 * expectedSimilarity : 1
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return multiplier;
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return multiplier
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}
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// Helper function to validate context lines against original content
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function validateContextLines(searchStr: string, content: string): number {
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// Extract just the context lines from the search string
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const contextLines = searchStr
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.split('\n')
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.filter((line) => !line.startsWith('-')); // Exclude removed lines
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const contextLines = searchStr.split("\n").filter((line) => !line.startsWith("-")) // Exclude removed lines
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// Compare context lines with content
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const similarity = evaluateSimilarity(contextLines.join('\n'), content);
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const similarity = evaluateSimilarity(contextLines.join("\n"), content)
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// Context lines must match very closely, or confidence drops significantly
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return similarity < MIN_CONFIDENCE ? similarity * 0.3 : similarity;
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// Get adaptive threshold based on content size
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const threshold = getAdaptiveThreshold(content.split("\n").length)
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// Calculate uniqueness boost
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const uniquenessScore = evaluateContentUniqueness(searchStr, content.split("\n"))
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const uniquenessBoost = uniquenessScore * UNIQUE_CONTENT_BOOST
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// Adjust confidence based on threshold and uniqueness
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return similarity < threshold ? similarity * 0.3 + uniquenessBoost : similarity + uniquenessBoost
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}
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// Exact match strategy
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export function findExactMatch(
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searchStr: string,
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// Helper function to create overlapping windows
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function createOverlappingWindows(
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content: string[],
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startIndex: number = 0
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): SearchResult {
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const contentStr = content.slice(startIndex).join('\n');
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const searchLines = searchStr.split('\n');
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searchSize: number,
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overlapSize: number = DEFAULT_OVERLAP_SIZE
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): { window: string[]; startIndex: number }[] {
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const windows: { window: string[]; startIndex: number }[] = []
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const exactMatch = contentStr.indexOf(searchStr);
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if (exactMatch !== -1) {
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const matchedContent = content
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.slice(
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startIndex + contentStr.slice(0, exactMatch).split('\n').length - 1,
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startIndex +
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contentStr.slice(0, exactMatch).split('\n').length -
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1 +
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searchLines.length
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)
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.join('\n');
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// Ensure minimum window size is at least searchSize
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const effectiveWindowSize = Math.max(searchSize, Math.min(searchSize * 2, MAX_WINDOW_SIZE))
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const similarity = getDMPSimilarity(searchStr, matchedContent);
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const contextSimilarity = validateContextLines(searchStr, matchedContent);
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const confidence = Math.min(similarity, contextSimilarity);
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// Ensure overlap size doesn't exceed window size
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const effectiveOverlapSize = Math.min(overlapSize, effectiveWindowSize - 1)
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return {
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index:
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startIndex + contentStr.slice(0, exactMatch).split('\n').length - 1,
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confidence,
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strategy: 'exact',
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};
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// Calculate step size, ensure it's at least 1
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const stepSize = Math.max(1, effectiveWindowSize - effectiveOverlapSize)
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for (let i = 0; i < content.length; i += stepSize) {
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const windowContent = content.slice(i, i + effectiveWindowSize)
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if (windowContent.length >= searchSize) {
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windows.push({ window: windowContent, startIndex: i })
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}
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}
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return { index: -1, confidence: 0, strategy: 'exact' };
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return windows
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}
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// Helper function to combine overlapping matches
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function combineOverlappingMatches(
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matches: (SearchResult & { windowIndex: number })[],
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overlapSize: number = DEFAULT_OVERLAP_SIZE
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): SearchResult[] {
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if (matches.length === 0) {
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return []
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}
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// Sort matches by confidence
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matches.sort((a, b) => b.confidence - a.confidence)
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const combinedMatches: SearchResult[] = []
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const usedIndices = new Set<number>()
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for (const match of matches) {
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if (usedIndices.has(match.windowIndex)) {continue}
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// Find overlapping matches
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const overlapping = matches.filter(
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(m) =>
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Math.abs(m.windowIndex - match.windowIndex) === 1 &&
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Math.abs(m.index - match.index) <= overlapSize &&
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!usedIndices.has(m.windowIndex)
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)
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if (overlapping.length > 0) {
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// Boost confidence if we find same match in overlapping windows
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const avgConfidence =
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(match.confidence + overlapping.reduce((sum, m) => sum + m.confidence, 0)) / (overlapping.length + 1)
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const boost = Math.min(0.05 * overlapping.length, 0.1) // Max 10% boost
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combinedMatches.push({
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index: match.index,
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confidence: Math.min(1, avgConfidence + boost),
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strategy: `${match.strategy}-overlapping`,
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})
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usedIndices.add(match.windowIndex)
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overlapping.forEach((m) => usedIndices.add(m.windowIndex))
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} else {
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combinedMatches.push({
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index: match.index,
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confidence: match.confidence,
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strategy: match.strategy,
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})
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usedIndices.add(match.windowIndex)
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}
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}
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return combinedMatches
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}
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// Modified search functions to use sliding windows
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export function findExactMatch(searchStr: string, content: string[], startIndex: number = 0): SearchResult {
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const searchLines = searchStr.split("\n")
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const windows = createOverlappingWindows(content.slice(startIndex), searchLines.length)
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const matches: (SearchResult & { windowIndex: number })[] = []
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windows.forEach((windowData, windowIndex) => {
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const windowStr = windowData.window.join("\n")
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const exactMatch = windowStr.indexOf(searchStr)
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if (exactMatch !== -1) {
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const matchedContent = windowData.window
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.slice(
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windowStr.slice(0, exactMatch).split("\n").length - 1,
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windowStr.slice(0, exactMatch).split("\n").length - 1 + searchLines.length
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)
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.join("\n")
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const similarity = getDMPSimilarity(searchStr, matchedContent)
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const contextSimilarity = validateContextLines(searchStr, matchedContent)
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const confidence = Math.min(similarity, contextSimilarity)
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matches.push({
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index: startIndex + windowData.startIndex + windowStr.slice(0, exactMatch).split("\n").length - 1,
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confidence,
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strategy: "exact",
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windowIndex,
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})
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}
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})
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const combinedMatches = combineOverlappingMatches(matches)
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return combinedMatches.length > 0 ? combinedMatches[0] : { index: -1, confidence: 0, strategy: "exact" }
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}
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// String similarity strategy
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export function findSimilarityMatch(
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searchStr: string,
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content: string[],
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startIndex: number = 0
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): SearchResult {
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const searchLines = searchStr.split('\n');
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let bestScore = 0;
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let bestIndex = -1;
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const minScore = 0.8;
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export function findSimilarityMatch(searchStr: string, content: string[], startIndex: number = 0): SearchResult {
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const searchLines = searchStr.split("\n")
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let bestScore = 0
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let bestIndex = -1
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const minScore = 0.8
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for (let i = startIndex; i < content.length - searchLines.length + 1; i++) {
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const windowStr = content.slice(i, i + searchLines.length).join('\n');
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const score = compareTwoStrings(searchStr, windowStr);
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const windowStr = content.slice(i, i + searchLines.length).join("\n")
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const score = compareTwoStrings(searchStr, windowStr)
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if (score > bestScore && score >= minScore) {
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const similarity = getDMPSimilarity(searchStr, windowStr);
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const contextSimilarity = validateContextLines(searchStr, windowStr);
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const adjustedScore = Math.min(similarity, contextSimilarity) * score;
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const similarity = getDMPSimilarity(searchStr, windowStr)
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const contextSimilarity = validateContextLines(searchStr, windowStr)
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const adjustedScore = Math.min(similarity, contextSimilarity) * score
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if (adjustedScore > bestScore) {
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bestScore = adjustedScore;
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bestIndex = i;
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bestScore = adjustedScore
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bestIndex = i
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}
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}
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}
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@@ -160,59 +279,136 @@ export function findSimilarityMatch(
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return {
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index: bestIndex,
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confidence: bestIndex !== -1 ? bestScore : 0,
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strategy: 'similarity',
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};
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strategy: "similarity",
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}
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}
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// Levenshtein strategy
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export function findLevenshteinMatch(
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searchStr: string,
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content: string[],
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startIndex: number = 0
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): SearchResult {
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const searchLines = searchStr.split('\n');
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const candidates = [];
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export function findLevenshteinMatch(searchStr: string, content: string[], startIndex: number = 0): SearchResult {
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const searchLines = searchStr.split("\n")
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const candidates = []
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for (let i = startIndex; i < content.length - searchLines.length + 1; i++) {
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candidates.push(content.slice(i, i + searchLines.length).join('\n'));
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candidates.push(content.slice(i, i + searchLines.length).join("\n"))
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}
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if (candidates.length > 0) {
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const closestMatch = closest(searchStr, candidates);
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const index = startIndex + candidates.indexOf(closestMatch);
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const similarity = getDMPSimilarity(searchStr, closestMatch);
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const contextSimilarity = validateContextLines(searchStr, closestMatch);
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const closestMatch = closest(searchStr, candidates)
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const index = startIndex + candidates.indexOf(closestMatch)
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const similarity = getDMPSimilarity(searchStr, closestMatch)
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const contextSimilarity = validateContextLines(searchStr, closestMatch)
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const confidence = Math.min(similarity, contextSimilarity)
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return {
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index,
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confidence: index !== -1 ? confidence : 0,
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strategy: 'levenshtein',
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};
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strategy: "levenshtein",
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}
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}
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return { index: -1, confidence: 0, strategy: 'levenshtein' };
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return { index: -1, confidence: 0, strategy: "levenshtein" }
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}
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// Helper function to identify anchor lines based on uniqueness and complexity
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function identifyAnchors(searchStr: string, content: string[]): { line: string; index: number; weight: number }[] {
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const searchLines = searchStr.split("\n")
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const contentStr = content.join("\n")
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const anchors: { line: string; index: number; weight: number }[] = []
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for (let i = 0; i < searchLines.length; i++) {
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const line = searchLines[i]
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if (!line.trim()) {continue} // Skip empty lines
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// Calculate line complexity (more special chars = more unique)
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const specialChars = (line.match(/[^a-zA-Z0-9\s]/g) || []).length
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const complexity = specialChars / line.length
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// Count occurrences in content
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const regex = new RegExp(line.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"), "g")
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const matches = contentStr.match(regex)
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const occurrences = matches ? matches.length : 0
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// Calculate uniqueness weight
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const uniquenessWeight = occurrences <= 1 ? 1 : 1 / occurrences
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const weight = uniquenessWeight * (0.7 + 0.3 * complexity)
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if (weight > 0.5) {
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// Only consider lines with high enough weight
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anchors.push({ line, index: i, weight })
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}
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}
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// Sort by weight descending
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return anchors.sort((a, b) => b.weight - a.weight)
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}
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// Helper function to validate anchor positions
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function validateAnchorPositions(
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anchors: { line: string; index: number }[],
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content: string[],
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searchLines: string[]
|
||||
): number {
|
||||
for (const anchor of anchors) {
|
||||
const anchorIndex = content.findIndex((line) => line === anchor.line)
|
||||
if (anchorIndex !== -1) {
|
||||
// Check if surrounding context matches
|
||||
const contextBefore = searchLines.slice(Math.max(0, anchor.index - 2), anchor.index).join("\n")
|
||||
const contextAfter = searchLines.slice(anchor.index + 1, anchor.index + 3).join("\n")
|
||||
const contentBefore = content.slice(Math.max(0, anchorIndex - 2), anchorIndex).join("\n")
|
||||
const contentAfter = content.slice(anchorIndex + 1, anchorIndex + 3).join("\n")
|
||||
|
||||
const beforeSimilarity = evaluateSimilarity(contextBefore, contentBefore)
|
||||
const afterSimilarity = evaluateSimilarity(contextAfter, contentAfter)
|
||||
|
||||
if (beforeSimilarity > 0.8 && afterSimilarity > 0.8) {
|
||||
return anchorIndex - anchor.index
|
||||
}
|
||||
}
|
||||
}
|
||||
return -1
|
||||
}
|
||||
|
||||
// Anchor-based search strategy
|
||||
export function findAnchorMatch(searchStr: string, content: string[], startIndex: number = 0): SearchResult {
|
||||
const searchLines = searchStr.split("\n")
|
||||
const anchors = identifyAnchors(searchStr, content.slice(startIndex))
|
||||
|
||||
if (anchors.length === 0) {
|
||||
return { index: -1, confidence: 0, strategy: "anchor" }
|
||||
}
|
||||
|
||||
// Try to validate position using top anchors
|
||||
const offset = validateAnchorPositions(anchors.slice(0, 3), content.slice(startIndex), searchLines)
|
||||
|
||||
if (offset !== -1) {
|
||||
const matchPosition = startIndex + offset
|
||||
const matchedContent = content.slice(matchPosition, matchPosition + searchLines.length).join("\n")
|
||||
const similarity = getDMPSimilarity(searchStr, matchedContent)
|
||||
const contextSimilarity = validateContextLines(searchStr, matchedContent)
|
||||
const confidence = Math.min(similarity, contextSimilarity) * (1 + anchors[0].weight * 0.1) // Boost confidence based on anchor weight
|
||||
|
||||
return {
|
||||
index: matchPosition,
|
||||
confidence: Math.min(1, confidence), // Cap at 1
|
||||
strategy: "anchor",
|
||||
}
|
||||
}
|
||||
|
||||
return { index: -1, confidence: 0, strategy: "anchor" }
|
||||
}
|
||||
|
||||
// Main search function that tries all strategies
|
||||
export function findBestMatch(
|
||||
searchStr: string,
|
||||
content: string[],
|
||||
startIndex: number = 0
|
||||
): SearchResult {
|
||||
const strategies = [
|
||||
findExactMatch,
|
||||
findSimilarityMatch,
|
||||
findLevenshteinMatch,
|
||||
];
|
||||
export function findBestMatch(searchStr: string, content: string[], startIndex: number = 0): SearchResult {
|
||||
const strategies = [findExactMatch, findAnchorMatch, findSimilarityMatch, findLevenshteinMatch]
|
||||
|
||||
let bestResult: SearchResult = { index: -1, confidence: 0, strategy: 'none' };
|
||||
let bestResult: SearchResult = { index: -1, confidence: 0, strategy: "none" }
|
||||
|
||||
for (const strategy of strategies) {
|
||||
const result = strategy(searchStr, content, startIndex);
|
||||
const result = strategy(searchStr, content, startIndex)
|
||||
console.log("Search result:", result)
|
||||
if (result.confidence > bestResult.confidence) {
|
||||
bestResult = result;
|
||||
bestResult = result
|
||||
}
|
||||
}
|
||||
|
||||
return bestResult;
|
||||
return bestResult
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user