The model ends a shape question with <diagram>{json}</diagram>; rag/services/diagram.py
splits it off before mathtext, validates it against a whitelist and stores it on
Message.diagram (ai_tutor.0006). It reaches the app in the SSE done event and in
conversation history. Prompt tutor-v6-diagrams; a hint next to the question (shape
words only) raised gpt-4.1-mini from 3/12 to 12/12 diagrams, 0/8 on arithmetic.
progress.0005 adds tedi (600 xp) to the companion catalog.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Two benchmark rounds (14 models on 30 problems, then gpt-4o-mini vs gpt-4.1-mini
on 102 problems under three prompt versions, hand-graded): gpt-4.1-mini scored
95-96/102 against 85-90 for gpt-4o-mini, at 0.12 vs 0.04 cents per answer.
The default now lives in settings so the deploy needs no .env edit.
The context note "in the book / is a decimal point, not a fraction" was written
for the upper-grade books but went to every grade, so gpt-4.1-mini computed
1/7 + 1/8 as 3.5. For primary grades the note now says / can be either, and a
reading rule sits next to the question itself (the same rule at the end of
the system prompt had no effect). Slash problems went from 11 to 14 of 17;
three are still wrong.
generate_answer now reports the provider's exact per-call cost, reasoning
tokens and whether the raw output broke the plain-text rule. The benchmark
fixes primary grades being sent with no book context, caches retrieval so
runs compare on identical context, and retries Qdrant timeouts.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Tutor (prompt tutor-v3-stepwise-plain): plain text instead of LaTeX/markdown,
with rag/services/mathtext.py converting whatever still slips through; full
step-by-step solutions with a check before the final answer; three tones by
grade; the last 6 messages of the conversation go to the model and follow-ups
are no longer served from another user's cache; book solutions the checker
disputes are kept out of the context; a questionId gives the tutor the
practice question and the student's wrong answer. /api/health reports when
the AI credit runs out. rag_model_benchmark compares chat models on verified
book problems (run on the server; credit ran out mid-run).
Map: only lessons that have questions count toward finishing an island,
lessonIds[0] / nextLessonId is always the next lesson to play, and a finished
island stays finished. The Seyar characters (sigma/delta/pi) are seeded.
Audit fixes, each reproduced before fixing:
- the plan is stored on the order, so paying a monthly order after starting
a yearly checkout no longer grants a year
- opening checkout no longer demotes an active subscription; renewals extend
from the current expiry; expired subscriptions are not reported active
- one discount code per order and only before the gateway; a 100% code
completes the purchase; lost Zarinpal confirmations are re-verified
- the unused /api/payment/webhook "mark paid" endpoint is removed
- XP counts only answers recorded on the server
- 6-digit OTP, each code dies after 5 wrong guesses; POST /api/auth/logout
- a guest who signs up keeps their account and progress (test phones excluded)
- affiliate commission on the first real payment; guests excluded
- parents with several children no longer get a 500
- SQLite runs transactions IMMEDIATE, since it ignores select_for_update
- admin login is rate-limited; display names drop phone numbers and links;
userQuestion is capped at 2000 characters
- server-side heart enforcement exists behind HEARTS_ENFORCED (off until the
frontend handles 403 no_hearts)
Not done on purpose: rotating the server's secret key and moving to Postgres.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
مهم‌ترین خواسته‌ی سینیور این بود که بسنجیم پاسخِ AI واقعاً یاد می‌دهد یا نه.
برای سنجیدن، اول باید پاسخ‌ها قابلِ ردیابی می‌شدند.
پرامپت‌ها پایه‌آگاه شدند: تا پیش از این «معلمِ دبستانی» برای پایه نهم هاردکد
بود، هم در تولیدِ پاسخ و هم در vision — یعنی دانش‌آموزِ نهم لحنِ ابتدایی
می‌گرفت. PROMPT_VERSION حالا روی هر پیام ذخیره می‌شود.
هر پاسخ حاشیه‌نویسی می‌شود: مدل، نسخه‌ی پرامپت، پایه، collection، مقدارِ
فیلتر، تعداد chunk، توکن، تأخیر و from_cache. فیدبک reason اختیاری گرفت تا
لایک/دیسلایک بعداً قابلِ تحلیل باشد («بشه چک کرد کدوم بهتر بوده»).
ai_feedback_report --by prompt_version نسبتِ رضایت را کنارِ مصرف/هزینه
می‌گذارد. rag_eval سوال‌های واقعی را از مسیرِ واقعی می‌پرسد، با
--retrieval-only برای اجرای ارزان.
rag/services/lesson_context.py از lessonId تنها پایه و فصل را درمی‌آورد؛
normalize_grade_title هم اختلافِ «پایه نهم» در دیتابیس با «ریاضی نهم» در
نامِ collection را می‌بندد — بدونِ آن مسیرِ gradeId بی‌سروصدا خالی برمی‌گشت.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Self-contained app; wired into settings/urls in the next commit. Data
stays in the parallel team's live Qdrant — nothing is copied into our DB.
apps.py
- RagConfig.
models.py
- LessonChunk: DB-of-record for one summarized lesson chunk
(original_text + summary + embedding_vector) mirrored as a single
Qdrant point; loose lesson_id/grade_id ints, no cross-app FK.
LessonChunkImage for future multimodal lessons.
migrations/0001_initial.py
- initial tables for the two models.
services/clients.py
- lazy, lru_cache'd OpenAI + Qdrant clients (Rag_backend builds them at
import time and crashes without keys). RagNotConfigured raised when a
key/lib is missing; Qdrant client uses check_compatibility=False for
the 1.19-client / 1.17-server minor version gap.
services/config.py
- rag_is_configured(), collection-name template, grade-title resolution
(RAG_GRADE_TITLE_OVERRIDES -> content.Grade.title), retrieval top-k.
services/known_collections.py
- exact live Qdrant catalog: 6 collection names, math chapter-number ->
exact "فصل N: ..." string tables per (subject, grade), and the
علوم/اجتماعی lesson-number -> title maps. filter_key_for_subject()
picks `chapter` for math, `lesson_number` otherwise;
resolve_math_chapter() maps a plain chapter number (Persian or Latin
digits) to the stored string.
services/embedding.py
- embed_text() pinned to OPENAI_EMBEDDING_MODEL (text-embedding-3-small,
1536d) so vectors match the other team's existing collections.
services/chunking.py
- split_text_into_chunks(), summarize_chunk() (gpt-4o-mini, JSON out);
plain exceptions instead of ninja HttpError.
services/qdrant_store.py
- ensure_collection(), upsert_points(), search(): the lesson filter
matches the value both as str and native, since Qdrant stores
lesson_number as int and chapter as string.
services/retrieval.py
- resolve_target() -> (collection, filter_key, filter_value), reused by
the cache key and debug block.
- retrieve_context() -> (chunks, meta): embed -> Qdrant search scoped to
one lesson/chapter -> top-k. Math payloads (no `summary`) fall back to
generalSolution/problem/solution; a 404 collection returns empty.
services/generation.py
- generate_answer() and stream_answer() (token iterator for SSE);
context-grounded Persian prompt that explains from retrieved text
rather than refusing.
services/pdf.py
- textbook PDF -> page images -> gpt-4o vision problem-type extraction
(adapted from Rag_backend mathapp); returns dicts, no on-disk JSON.
pymupdf/pdf2image optional — missing libs raise RagNotConfigured.
services/ingest.py
- ingest_text() / ingest_pdf(): chunk -> summarize -> embed -> persist
LessonChunk + upsert Qdrant, in one transaction.
services/answer_cache.py
- short-TTL cache of the final answer keyed by
sha1(collection | filter_value | normalized-question).
services/tutor.py
- thin answer_question() (retrieve -> generate) for direct callers.
serializers.py, urls.py, views.py
- POST /api/rag/ingest/text, POST /api/rag/ingest/pdf (HasInternalServiceKey,
throttle scope rag_ingest), GET /api/rag/chunks review list.
RagProviderUnavailable -> clean 503.
admin.py
- LessonChunk admin with an inline for its images.
management/commands/rag_catalog.py
- read-only live Qdrant listing (--values, --json); never writes.
tests.py
- mocked-client tests: ask-with-retrieval, ingest, SSE stream, answer
cache, and rag_is_configured() gating.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
