whaleprep/DATA.md
dev cc0cd75ff9 Document NUM exclusion rationale (audited, not assumed)
Integer explanations end with the answer but phrasing varies enough
that regex extraction would mis-key questions. A wrong key is worse
than no question. NUM stays hand-added until NTA keys transcribed.
2026-10-13 00:02:53 +05:30

71 lines
3.2 KiB
Markdown
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

# Data sourcing — JEE Main question bank
## Status
**`scripts/import_examgoal.py` imports 9,813 real JEE Main PYQs** (2002–2025,
all three subjects) from the MIT-licensed
[jee_mains_pyqs_data_base](https://github.com/HostServer001/jee_mains_pyqs_data_base)
release asset (`1762787474-DataBaseChapters-v007.pkl`, ~24 MB, 14,973 questions
total). The keep rule is: type=mcq, fully text (no `<img>` anywhere in
stem/options/explanation), exactly one correct option in A–D. Skipped: 1,909
image-questions, 2,875 answer-less, 19 multi-correct, ~357 with stray images
in options/explanations.
```
python3 scripts/import_examgoal.py path/to/app.db [--pkl db.pkl]
```
Safe to re-run: INSERTs skip stems already present, so the 24 hand-written
seed questions are never duplicated. Existing deployments pick up the new
`CREATE INDEX` statements on boot (`init_db` runs them idempotently).
The demo seed (`src/db.rs`, 24 hand-written questions) remains as the offline
fallback when the pickle cannot be fetched. `scripts/expand_seed.py` is kept
for synthetic load testing only.
## Attribution
Question content: [HostServer001/jee_mains_pyqs_data_base](https://github.com/HostServer001/jee_mains_pyqs_data_base)
(MIT), itself reverse-engineered from a subscription site's API and cached.
Upstream model credit: examgoal database schema. No NTA PDFs were scraped —
NTA publishes image-rendered PDFs with no machine-readable question text
(`pdftotext` yields only question IDs).
## Schema mapping
| pickle field | our column |
|---|---|
| `chapterGroup` (11 groups) | `chapters.name` |
| `chapter` (91 slugs) | `subtopics.name` |
| `topic` (472 slugs) | folded into subtopic name when distinct |
| `correct_options: ["X"]` | `answer` |
| `question.en.content` (HTML+`$$`) | `stem` (sanitized) |
| `options[].content` | `opt_a..opt_d` (sanitized) |
| `explanation` | `solution` (sanitized) |
| `paperTitle` Morning/Evening | `shift` S1/S2 (examDate hour as fallback) |
| `difficulty` easy/medium/hard | `Easy`/`Medium`/`Hard` |
## Sanitizer contract (both sides match)
Write side (`import_examgoal.py::sanitize`) and read side
(`views.rs::rich`) share one allowlist: `br p sup sub b strong i em u ol ul
li table tbody thead tr th td span h3`, attributes always stripped,
`<picture>/<source>/<style>/<col>` blocks dropped whole. KaTeX renders
`$$..$$` display math and `\(..\)` inline at page load with `trust: false`.
## Adding more questions later
Re-run the importer against a newer pickle release. For NUM questions the
upstream data carries no answers, so they are excluded until a verified
answer source exists. The `answer` column for MCQ is a single letter.
## Why NUM answers are not extracted from explanations
Audited 2026-10-12: integer-question explanations do end with the numeric
result (`= 80`, `Total 11`, `is 8`), but the phrasing varies per question
(`Hence`, `Clearly`, `no. of integers in S is`, bare `= X` mid-derivation)
and several tails contain multiple numbers. A regex would mis-key a
meaningful fraction, and a wrong answer key teaches the wrong result —
worse than no question. NUM import stays out until NTA final answer keys
(authoritative per-shift source) are transcribed. The grader already
supports NUM with 0.1% tolerance for hand-added questions.