24fb00aef2
The job ID was pushed to extraction:user_jobs:{uid} twice:
1. quizzes.py router when dispatching the job
2. quiz_tasks.py Celery task when it starts running
Removed the push from the Celery task. Router handles it.
Also cleaned 13 duplicate entries from Redis.
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
269 lines
12 KiB
Python
269 lines
12 KiB
Python
"""Async quiz extraction task with step-by-step progress reporting via Redis."""
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import json
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import logging
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import time
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import os
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from app.tasks import celery_app
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from app.database import SessionLocal
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logger = logging.getLogger(__name__)
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EXPIRE_SECONDS = 3600
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CHUNK_PAGES = 50
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def _redis():
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import redis
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from app.config import settings
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return redis.from_url(settings.REDIS_URL, decode_responses=True)
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def _push_step(r, job_id: str, step: str, message: str):
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key = f"extraction:steps:{job_id}"
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entry = json.dumps({"step": step, "message": message, "ts": time.time()})
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r.rpush(key, entry)
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r.expire(key, EXPIRE_SECONDS)
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def _normalize_ocr(text: str) -> str:
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"""Fix common OCR artifacts in PREP PDFs."""
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return (text
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.replace("Pref erred", "Preferred")
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.replace("Pre ferred", "Preferred")
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.replace("Prefer red", "Preferred")
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.replace("ltem", "Item")
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.replace("ltcm", "Item"))
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@celery_app.task(name="extract_quiz", bind=True)
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def extract_quiz(
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self,
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job_id: str,
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user_id: int,
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section_id: int,
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title: str,
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mode: str,
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time_limit_minutes: int | None,
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model_id: str | None,
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question_category_id: int | None,
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extraction_mode: str = "standard",
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):
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r = _redis()
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r.set(f"extraction:status:{job_id}", "running", ex=EXPIRE_SECONDS)
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db = SessionLocal()
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try:
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from app.models.section import Section
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from app.models.pdf_document import PDFDocument
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from app.services import ai_service, vector_service, pdf_service, embedding_service
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from app.models.quiz import Quiz
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from app.models.question import Question
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from app.models.quiz_question_link import QuizQuestionLink
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from app.config import settings
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_push_step(r, job_id, "start", "Starting extraction…")
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section = db.query(Section).filter(Section.id == section_id).first()
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if not section:
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raise ValueError("Section not found")
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document = db.query(PDFDocument).filter(PDFDocument.id == section.document_id).first()
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if not document:
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raise ValueError("Document not found")
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total_pages = section.end_page - section.start_page + 1
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_push_step(r, job_id, "text", f"Loading text from pages {section.start_page}–{section.end_page} ({total_pages} pages)…")
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# Determine model
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if model_id:
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from app.models.ai_model_config import AIModelConfig
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config = db.query(AIModelConfig).filter(AIModelConfig.model_id == model_id).first()
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api_key = config.api_key if config and config.api_key else None
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model_name = config.name if config else model_id
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else:
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model_id, api_key = ai_service.get_model_for_task(db, "extraction")
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model_name = model_id
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# Split into 50-page chunks
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if total_pages <= CHUNK_PAGES:
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chunks = [(section.start_page, section.end_page)]
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else:
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chunks = []
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p = section.start_page
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while p <= section.end_page:
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end = min(p + CHUNK_PAGES - 1, section.end_page)
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chunks.append((p, end))
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p = end + 1
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n_chunks = len(chunks)
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if n_chunks > 1:
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_push_step(r, job_id, "text", f"Large section: splitting into {n_chunks} chunks of up to {CHUNK_PAGES} pages each.")
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all_valid_questions = []
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all_skipped = []
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# ── Non-standard extraction modes ─────────────────────────────────────
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if extraction_mode != "standard":
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from app.services.extraction_modes import (
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extract_questions_only, extract_two_step,
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extract_with_regex, ai_decide_strategy,
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)
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resolved_mode = extraction_mode
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if extraction_mode == "ai_decide":
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_push_step(r, job_id, "ai", "AI is analysing the document to choose the best strategy…")
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resolved_mode, reasoning = ai_decide_strategy(
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section.document_id, section.start_page, section.end_page,
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model_id, api_key,
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)
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_push_step(r, job_id, "ai", f"AI chose: {resolved_mode} — {reasoning}")
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if resolved_mode == "questions_only":
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_push_step(r, job_id, "ai", "Mode: Questions Only — extracting questions without answers.")
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for chunk_idx, (start_p, end_p) in enumerate(chunks, 1):
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_push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: pages {start_p}–{end_p}…")
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chunk_content = vector_service.get_pages_text(
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document_id=section.document_id, start_page=start_p, end_page=end_p)
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if not chunk_content:
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continue
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try:
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qs = extract_questions_only(_normalize_ocr(chunk_content),
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f"{start_p}-{end_p}", start_p, model_id, api_key)
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all_valid_questions.extend(qs)
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_push_step(r, job_id, "ai", f" Pages {start_p}–{end_p}: {len(qs)} questions. Total: {len(all_valid_questions)}.")
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except Exception as e:
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_push_step(r, job_id, "ai", f" Pages {start_p}–{end_p} failed: {e}")
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elif resolved_mode == "two_step":
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_push_step(r, job_id, "ai", "Mode: Two-Step (separate answer key section).")
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all_valid_questions, all_skipped = extract_two_step(
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section.document_id, section.start_page, section.end_page,
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model_id, api_key,
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push_step=lambda step, msg: _push_step(r, job_id, step, msg),
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chunk_pages=CHUNK_PAGES,
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)
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elif resolved_mode == "regex":
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_push_step(r, job_id, "ai", "Mode: AI+Regex — analysing format then applying regex.")
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all_valid_questions, all_skipped = extract_with_regex(
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section.document_id, section.start_page, section.end_page,
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model_id, api_key,
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push_step=lambda step, msg: _push_step(r, job_id, step, msg),
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chunk_pages=CHUNK_PAGES,
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)
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else:
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# ai_decide resolved to standard — fall through to standard loop below
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extraction_mode = "standard"
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if extraction_mode == "standard":
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# ── STANDARD: existing working extraction (unchanged) ──────────────
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for chunk_idx, (start_p, end_p) in enumerate(chunks, 1):
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if n_chunks > 1:
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_push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: pages {start_p}–{end_p} → {model_name}…")
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else:
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_push_step(r, job_id, "ai", f"Sending pages {start_p}–{end_p} to {model_name}…")
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chunk_content = vector_service.get_pages_text(
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document_id=section.document_id, start_page=start_p, end_page=end_p,
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)
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if not chunk_content:
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_push_step(r, job_id, "ai", f" No text found for pages {start_p}–{end_p}, skipping.")
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continue
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try:
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chunk_data = ai_service.extract_questions(
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_normalize_ocr(chunk_content),
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page_info=f"{start_p}-{end_p}",
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page_ref=start_p,
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model_id=model_id,
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api_key=api_key,
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)
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chunk_skipped = chunk_data[0].pop("skipped", []) if chunk_data else []
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chunk_valid = [q for q in chunk_data if q.get("correct_answer")]
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all_valid_questions.extend(chunk_valid)
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all_skipped.extend(chunk_skipped)
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_push_step(r, job_id, "ai",
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f" Pages {start_p}–{end_p}: {len(chunk_valid)} questions"
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f"{f', {len(chunk_skipped)} skipped' if chunk_skipped else ''}. "
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f"Total: {len(all_valid_questions)}.")
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except Exception as e:
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_push_step(r, job_id, "ai", f" Pages {start_p}–{end_p} failed: {e}. Continuing…")
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valid_questions = all_valid_questions
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skipped = all_skipped
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_push_step(r, job_id, "ai", f"Extraction complete: {len(valid_questions)} valid questions{f', {len(skipped)} skipped' if skipped else ''}.")
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if not valid_questions:
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raise ValueError("No valid questions extracted. The AI could not find questions with correct answers in this page range.")
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# Extract images
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_push_step(r, job_id, "images", "Extracting question images…")
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file_path = os.path.join(settings.UPLOAD_DIR, document.filename)
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page_images = {}
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if os.path.exists(file_path):
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try:
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page_images = pdf_service.extract_all_images(
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file_path, document.id, section.start_page, section.end_page
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)
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except Exception as e:
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_push_step(r, job_id, "images", f"Image extraction skipped: {e}")
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# Create quiz
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quiz = Quiz(
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section_id=section_id,
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user_id=user_id,
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title=title,
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questions_count=len(valid_questions),
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mode=mode,
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time_limit_minutes=time_limit_minutes,
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skipped_questions=json.dumps(skipped) if skipped else None,
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)
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db.add(quiz)
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db.flush()
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_push_step(r, job_id, "save", f"Saving {len(valid_questions)} questions and generating embeddings…")
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for pos, q in enumerate(valid_questions):
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page_ref = q.get("page_reference")
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image_path = None
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if page_ref and page_ref in page_images and page_images[page_ref]:
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image_path = page_images[page_ref].pop(0)
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if not page_images[page_ref]:
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del page_images[page_ref]
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question = Question(
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source_quiz_id=quiz.id,
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question_category_id=question_category_id,
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question_text=q["question_text"],
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question_type=q["question_type"],
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options=q.get("options"),
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correct_answer=q["correct_answer"],
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explanation=q.get("explanation", ""),
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page_reference=page_ref,
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image_path=image_path,
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)
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db.add(question)
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db.flush()
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db.add(QuizQuestionLink(quiz_id=quiz.id, question_id=question.id, position=pos))
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try:
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embedding_service.embed_question(question)
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except Exception as e:
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logger.warning(f"Embedding failed for question {question.id}: {e}")
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db.commit()
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db.refresh(quiz)
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_push_step(r, job_id, "done", f"Quiz ready! {len(valid_questions)} questions extracted and saved.")
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r.set(f"extraction:status:{job_id}", "completed", ex=EXPIRE_SECONDS)
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r.set(f"extraction:quiz_id:{job_id}", str(quiz.id), ex=EXPIRE_SECONDS)
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return quiz.id
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except Exception as e:
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logger.exception(f"Quiz extraction failed for job {job_id}")
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_push_step(r, job_id, "error", f"Extraction failed: {e}")
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r.set(f"extraction:status:{job_id}", "failed", ex=EXPIRE_SECONDS)
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r.set(f"extraction:error:{job_id}", str(e)[:500], ex=EXPIRE_SECONDS)
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raise
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finally:
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db.close()
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