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fix(elos): better grounded answers on real course slides; faster MCP sources - #24
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…sources
Found by running the tutor on a real 226-file course library:
- answers now rerank BM25 hits: duplicates removed, slide titles and quiz
questions penalised, explanatory/definition passages favoured, oral
transcripts ranked after written material; quoted sentences deduplicated
- LaTeX-made PDFs: spacing accents recomposed ("exprim ́ ee" → "exprimée")
- MCP server: extraction cached per file (size + mtime), 1,000-file cap,
first folder = subject, second = origin (first load ~10 s for ~4,000
pages, then ~7 ms)
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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| export function cleanExtractedText(text: string): string { | ||
| return text | ||
| // LaTeX-made PDFs extract accents as separate marks before the letter ("exprim ́ ee" → "exprimée"). | ||
| .replace(/ ?([\u00B4\u0060\u02C6\u00A8\u0300-\u0302\u0308]) ?([aeiouAEIOU])/g, (_match, mark: string, letter: string) => (letter + SPACING_ACCENTS[mark]).normalize("NFC")) |
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When Markdown or text contains code whose opening backtick precedes a vowel, such as `echo`, `awk`, or `uname`, this replacement treats U+0060 as a grave accent and also consumes the preceding space, producing corrupted strings such as ècho, àwk, and ùname merged into surrounding text. Because cleanExtractedText processes Markdown and plain-text sources as well as PDF extraction, this damages common commands, citations, and retrieval terms; restrict the repair to extraction-specific accent contexts rather than treating every ASCII backtick as an accent.
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| return splitSentences(hit.passage.text) | ||
| .map((sentence, index) => ({ sentence, index, overlap: tokenize(sentence).filter((token) => terms.has(token)).length })) | ||
| .filter((item) => item.overlap > 0) | ||
| .filter((item) => item.overlap > 0 && !isQuestionLike(item.sentence) && item.sentence.split(/\s+/).length >= 4) |
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When the only relevant source sentence has fewer than four whitespace-separated words, such as TCP est fiable., the passage passes retrieval and answerability but this filter removes its sole sentence, causing answerFromSources to report that the information is absent. The previous implementation returned such exact source answers, so title suppression should not use an unconditional four-word minimum for otherwise declarative, relevant sentences.
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Tuning from a real 226-file course library (slides, TPs, transcripts):
Real-library probe: 7/8 course questions answered with relevant cited passages, off-topic question refused.
Test plan
npm test647/647, typecheck,eslint ., build🤖 Generated with Claude Code