I need a Telegram bot that listens to questions posted in a channel or group and instantly finds the closest‐matching multiple-choice items from nine medical textbooks I own. All reference questions are in PDF files, so the first task is to parse those books, pull out each stem, options and any author-provided explanation, then store everything in a searchable index. Once the database is ready, the bot should accept an “imperfect” MCQ—anything from a photo-to-text snippet to a paraphrased plain-text question—run a similarity check and return every match whose score exceeds a configurable threshold. Each hit must show: • the similarity percentage • the full textbook question and its options • any explanation that is actually present in the book (if none exists, the bot simply skips that part) A single top match isn’t enough; the user should see all qualifying matches, ordered by score, so they can judge which one is truly relevant. Key points to cover in your build • Accurate PDF text extraction, including tables or special medical symbols where they appear • Robust fuzzy-matching / NLP logic capable of coping with typos and partial wording • Easy deployment to an existing Telegram channel, with commands for start, help, set-threshold and reindex • Clean, commented code (Python + aiogram, Node, or another mainstream stack you prefer) plus a brief setup guide so I can run the bot on my VPS or a cloud function Acceptance will be based on a live demo in my test channel, correct similarity scoring on a sample set I provide, and clear hand-over of all source code and instructions.