بالا آوردنِ نسخه‌ی جدید تا الان چند کارِ دستی به ترتیبِ درست بود (pull، نصبِ
وابستگی‌ها، مهاجرت، استاتیک، ری‌استارت) و فراموش شدنِ هر کدام اپ را می‌خواباند
با خطایی که از بیرون فقط «502 Bad Gateway» دیده می‌شد. همین الان هم سرور
دقیقاً به همین دلیل خوابیده: کامیتِ ac1b1af پکیجِ django-cors-headers را اضافه
کرد، و بدونِ pip install جنگو با ModuleNotFoundError بالا نمی‌آید.
deploy.sh همه را به ترتیب و idempotent انجام می‌دهد. pip install همیشه اجرا
می‌شود، نه فقط وقتی requirements عوض شده — تشخیصِ «عوض شده یا نه» خودش یک جای
فراموش‌شدنیِ دیگر است. آخرِ کار خودش سلامت را بررسی می‌کند و اگر بالا نیامده
باشد علت‌های رایج را چاپ می‌کند، به‌جای اینکه کسی بعداً از بیرون کشفش کند.
GET /api/health وضعیت را بدونِ احراز و بدونِ SSH بیرون می‌دهد: دیتابیس،
مهاجرت‌های اعمال‌نشده، کش، و شمارشِ پایه/درس/سوال روی همان سرور. مهم‌ترینش
مهاجرتِ اعمال‌نشده است — حالتی که اپ ظاهراً بالا می‌آید و بعد زیرِ بارِ واقعی
با خطای ستونِ ناموجود می‌شکند. عمداً هیچ سکرتی برنمی‌گرداند، فقط بله/خیرِ
«تنظیم شده».
۵۰۳ برمی‌گرداند وقتی دیتابیس/کش/مهاجرت خراب است تا پایشِ خودکار بتواند رویش
حساب کند؛ نبودِ محتوا خرابی حساب نمی‌شود ولی گزارش می‌شود.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
خطاهای این پروژه خاموش‌اند: نامِ اشتباهِ collection یا فیلترِ نخورده نتیجه‌ی
خالی می‌دهد نه استثنا، و دستیار فقط بدتر جواب می‌دهد. این تست‌ها نگهبانِ
همان‌جاهایند.
TextbookLayoutTests بررسی می‌کند رشته‌ی هر فصل بینِ ingest و retrieval یکی
باشد و بازه‌ی صفحه‌ها پیوسته بماند. IngestBookResumeTests مطمئن می‌شود
صفحه‌ی بی‌مسئله دوبار پول نمی‌گیرد، صفحه‌ی خطادار در اجرای بعد دوباره
سراغش می‌روند، و upsertِ شکست‌خورده rollback می‌شود.
LessonContextResolutionTests نگهبانِ نرمال‌سازیِ «پایه نهم» → «ریاضی نهم»
است و اینکه مسیرِ gradeId و مسیرِ lessonId به یک collection برسند.
GradeAwarePromptTests جلوی برگشتنِ لحنِ دبستانی برای پایه‌های بالا را
می‌گیرد. FollowUpStaysGroundedTests پیامِ دومِ یک گفتگو را هم مستند نگه
می‌دارد.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
سینیور خواست زودتر به فرانت‌اند وصل شویم تا از آنجا هم تست شود. این‌ها
سمتِ ما لازم بودند:
CORS با allowlist صریح (نه ALLOW_ALL): احرازِ هویتِ ما توکنِ Bearer در هدر
است، پس origin ناشناس نباید از مرورگرِ کاربر به API ما بزند.
CORS_ALLOW_CREDENTIALS عمداً خاموش می‌ماند — کوکی‌ای در کار نیست و روشن
کردنش فقط سطحِ حمله اضافه می‌کند. CorsMiddleware بالاتر از CommonMiddleware
گذاشته شد وگرنه ریدایرکتِ APPEND_SLASH و خطای ۴۰۰ هدرِ CORS نمی‌گیرند و در
مرورگر به‌شکلِ خطای مبهمِ شبکه دیده می‌شوند.
PATCH /api/user/profile اضافه شد؛ تا پیش از این هیچ راهی برای ثبتِ نامِ
کاربر نبود. GET /api/home بدونِ پارامتر حالا پایه‌ی ذخیره‌شده‌ی کاربر را
برمی‌گرداند و POST /api/ai/ask فقط با lessonId کار می‌کند — هر دو برای
اینکه فرانت لازم نباشد پایه را خودش نگه دارد. questionCount به
lessons/{id}/intro اضافه شد.
docs/frontend/api.ts و README.md کلاینتِ تایپ‌دار و راهنمای اتصال‌اند، از
روی OpenAPI واقعیِ همین سرویس تولید شده‌اند.
Co-Authored-By: Claude Opus 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>
تا پیش از این کلِ محتوای دیتابیس ردیف‌های [DEMO] و [SEED] بود — دانش‌آموزِ
پایه نهم در دمو «جمع تا ۱۰» می‌دید. سینیور «یک بیسیکِ کامل با دیتای واقعی»
خواسته بود.
seed_curriculum_from_textbook (رایگان، هیچ فراخوانیِ پولی ندارد):
Grade ← پایه‌ی کتاب
Unit ← فصلِ کتاب
Node ← درسِ داخلِ فصل، از فهرستِ خودِ کتاب
Lesson ← نوعِ مسئله‌ی استخراج‌شده در بازه‌ی صفحه‌ی همان درس
intro_text مستقیم از generalSolution کتاب می‌آید.
generate_questions_from_chunks سوالِ چهارگزینه‌ای با گزینه‌های انحرافیِ
باورپذیر می‌سازد (هزینه‌دار، اعتبارسنجیِ سخت‌گیرانه). سوال اینجا جدا شد
چون ساختنِ گزینه‌ی انحرافیِ درست به مدل نیاز دارد.
purge_demo_data ردیف‌های [SEED]/[DEMO] را گزارش و — فقط با --apply — پاک
می‌کند؛ بدونِ آن این ردیف‌ها در دمو داخلِ API دیده می‌شوند.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
نقشه‌ی فیزیکیِ کتاب‌ها (افستِ صفحه، بازه‌ی هر فصل، درس‌های داخلِ فصل از
فهرستِ خودِ کتاب) در rag/services/textbooks.py ثبت شد تا عنوانِ فصل از
کتاب بیاید نه از حدسِ مدل — رشته‌ی فصل باید دقیقاً بینِ ingest و retrieval
یکی باشد وگرنه نتیجه بی‌سروصدا خالی برمی‌گردد.
rag_ingest_book کتاب را فصل‌به‌فصل ingest می‌کند و قابلِ ازسرگیری است:
rag.IngestedPage نشانه‌ی «این صفحه پردازش شد» را نگه می‌دارد حتی وقتی
صفحه chunkی نساخته، وگرنه هر بار اجرای دوباره بابتِ صفحه‌های بی‌مسئله
دوباره پول می‌گرفت. --plan هزینه را رایگان برآورد می‌کند.
rag_import_collection نقاطِ موجودِ Qdrant را در LessonChunk می‌ریزد —
فقط می‌خواند، رایگان و idempotent، برای پایه‌هایی که تیمِ دیگر ingest
کرده بود.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Two bugs made self-ingested content useless, both silently:
- _payload wrote `chapter_number` (int) while math retrieval filters on
`chapter` (the full "فصل ۲: کسر" string), so anything we ingest could
never match a search — no error, just permanently invisible content.
- ingest_pdf flattened the vision extraction into one text blob and threw
away type/keywords/generalSolution/problem/solution. LessonChunk is the
source of truth, so rebuilding meant paying for vision again — and the
problem/solution pairs are the input for building lesson questions.
LessonChunk now stores those fields and _payload emits them under the same
keys the other team's collections use, so ours and theirs stay one shape.
Also fixes a regression from the whitenoise commit: the Manifest storage
variant raises when staticfiles.json is absent, which broke all 23 admin
rendering tests (Django forces DEBUG=False in tests, so USE_FINDERS is off)
and would have taken the admin panel down entirely the day DEBUG=0 ships
without a collectstatic run.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Admin/DRF/Swagger CSS and JS never load under gunicorn since Django's
staticfiles app only auto-serves through `runserver`. WHITENOISE_USE_FINDERS
is tied to DEBUG so no collectstatic step is needed while DEBUG=1.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Admin login (and any POST form) fails with "Origin checking failed"
behind a proxy that terminates TLS, since Django had no trusted-origins
list configured at all.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
New progress.Character model: an admin-managed catalog (same pattern as
subscription.Plan) of selectable mascots, each unlocked once the user's
XP crosses unlock_xp. No separate ownership table yet — unlocking is a
pure function of UserStats.xp, kept intentionally minimal since this is
the first of an incremental rollout the senior asked for.
- GET /api/characters — catalog with per-user unlocked/selected flags
- POST /api/characters/select — picks an active character, rejecting
ones the user hasn't unlocked yet
- UserStats.selected_character_id tracks the pick
- seed_test_data now seeds 4 sample characters (owl/fox/turtle/lion)
for local/staging testing
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
payment.services.call_payment_gateway was a stub returning a fake
payment.gateway/pay/{id} URL. It's replaced with a real Zarinpal
integration (payment/zarinpal.py, Payment API v4):
- create_order/CreateOrderView still never call Zarinpal directly —
paymentUrl points at our own GET /api/payment/orders/{id}/pay
(PaymentRedirectView), which requests the real Zarinpal authority on
first visit and redirects to StartPay. This keeps order creation from
depending on Zarinpal's uptime/latency, and means none of the existing
purchase/idempotency tests needed to change.
- GET /api/payment/zarinpal/callback (ZarinpalCallbackView) is where
Zarinpal redirects the user back to; it always re-verifies
server-to-server via zarinpal.verify_payment before marking an order
paid, so a forged Status=OK in the query string alone does nothing.
- zarinpal.py fails closed (503 / invalid) when ZARINPAL_MERCHANT_ID
isn't configured, matching payment/bazaar.py's existing convention —
never a fake successful payment.
Adds Order.gateway_authority, PUBLIC_BASE_URL/ZARINPAL_* settings, and
a payment_pay throttle scope.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
next_refill_at was set on UserHearts but never read or recalculated,
so a heart lost to zero never came back. UserHearts.apply_regen() now
catches up missed 24h intervals on read/deduct, and lose_heart() starts
the timer only when it isn't already running.
Any user with an active (non-expired) subscription now skips heart
deduction entirely, regardless of which plan they're on — good enough
for now until per-plan perks are needed.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The skill-map response gave units -> nodes with id/title/status but no way
to map a node to its lessons, and no other endpoint fills that gap
(/api/progress/nodes doesn't either). Any client rendering the map needs
this to know which lesson to open when a node is tapped.
The ids are already computed in the cached _grade_tree; this just surfaces
them. Additive and backward-compatible; content tests pass.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
rag/services/tutor.py
- deleted: answer_question() had no callers (AskAIView calls
retrieve_context + generate_answer directly) and carried an unused
import.
rag/services/config.py
- docstring: drop the claim that the lesson-filter key is settings-driven
(it is auto-selected per subject in known_collections.py).
.env.example
- drop the leftover note about the removed RAG_QDRANT_LESSON_FILTER_KEY.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
subscription/views.py
- PlansListView: served from cache (subscription:plans:v1, 1h TTL);
plans change ~never and the list is read on every subscription screen.
subscription/receivers.py (new)
- post_save/post_delete on Plan -> cache.delete(PLANS_CACHE_KEY).
subscription/apps.py
- SubscriptionConfig.ready() imports receivers.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
TadrisinoTokenAuthentication ran one SELECT on accounts_user for every
authenticated request. Off by default (AUTH_USER_CACHE_TTL=0) so behaviour
is unchanged until a deployment opts in.
accounts/authentication.py
- _get_request_user(): when AUTH_USER_CACHE_TTL>0, cache all concrete
field values under auth:user:<id> and rebuild a full User instance
(so save(update_fields=[...]) stays safe); otherwise load from DB as
before. invalidate_user_cache() / _load_user_or_fail() helpers.
accounts/receivers.py (new)
- post_save/post_delete on User -> invalidate_user_cache(pk); covers
every in-app write path (SetUserGrade, login, refresh, last_activity).
accounts/apps.py
- AccountsConfig.ready() imports receivers.
accounts/tests.py
- AuthUserRowCacheTests: disabled -> loads every request; enabled ->
loads once then cache; User.save() invalidates; SetUserGradeView still
persists and the next request sees the new grade.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
progress/views.py
- GuestAwareReadThrottle (same guest-by-IP pattern as the other apps);
throttle_scope='read' on UserSummaryView, StreakCalendarView,
LeaderboardView.
notifications/views.py
- GuestAwareReadThrottle + throttle_scope='read' on NotificationListView.
- list capped at NOTIFICATIONS_MAX=100 newest (Meta.ordering already
newest-first); unreadCount still counted over the full set.
notifications/tests.py
- 150 rows -> 100 returned newest-first, unreadCount = 75.
rag/views.py
- ChunkListView: explicit order_by('-id') before the existing [:200] cap.
ai_tutor/views.py
- ConversationMessagesView: return the newest 200 messages in
chronological order instead of the whole conversation.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
GET /api/home is the most-hit authenticated endpoint (every app open) and
was running one COUNT per node. Now it is a flat number of queries
regardless of grade size, and the shared curriculum skeleton is served
from cache; per-user progress/stats stay live.
content/views.py
- _node_status(): takes a precomputed set of completed lesson_ids and a
node's lesson_ids instead of firing its own COUNT query.
- HomeView.get(): one query for all completed lesson_ids of the user in
the grade, status computed in Python; grade tree (units -> nodes ->
lesson_ids, no per-user data) read from cache
(content:tree:grade:{id}:v{N}); missing grade -> 404 (was an
unhandled 500); grade with no units -> empty units, not 404.
- GradesListView: served from cache (content:grades:v{N}).
- curriculum_version() / bump_curriculum_version(): one shared version
key; bumping it invalidates every content cache key at once.
- both GET views get throttle_scope='read' + GuestAwareContentThrottle.
content/receivers.py
- post_save/post_delete on Grade/Unit/Node/Lesson -> bump_curriculum_version().
content/tests.py
- HomeViewScalabilityTests: query count flat for 2 vs 20 nodes; 2nd call
hits cache (no content_unit/node SQL); a new Node invalidates the tree;
missing grade 404; empty grade 200.
- GradesListCacheTests: list cached, invalidated on new Grade.
- ReadThrottleTests: /api/home returns 429 past the limit.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
settings.py
- DATABASES.default.CONN_MAX_AGE from DB_CONN_MAX_AGE env (default 60);
harmless on SQLite, saves a TCP+auth handshake per request on Postgres.
- DEFAULT_RENDERER_CLASSES: BrowsableAPIRenderer only when DEBUG — no
HTML API browser or its overhead in production.
- DEFAULT_THROTTLE_RATES['read'] = READ_THROTTLE_RATE env (default
120/min): a cheap ceiling for the hot GET endpoints.
- AUTH_USER_CACHE_TTL env (default 0 = off): TTL for the per-request
user-row cache added in a later commit; 0 keeps current behaviour.
.env.example
- document DB_CONN_MAX_AGE / AUTH_USER_CACHE_TTL / READ_THROTTLE_RATE.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
README.md
- `rag` app row in the app table.
- new "دستیار هوش مصنوعی (RAG)" subsection: what /api/ai/ask does now,
the OPENAI_/QDRANT_ keys it needs, how to test it in Swagger, the
stream flag, rag_catalog, and the internal ingest endpoints.
- production-notes bullet updated: /api/ai/ask is real RAG when keys are
set, no longer "stub only".
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The math-app frontend prototype drives a RobotMascot with a fixed set of
moods; this gives that animation an explicit backend signal. Complements
the existing Lesson.mascot_animation / LessonIntro.mascotAnimation, which
are unchanged.
serializers.py
- AnswerResponseSerializer: new mascotMood field.
views.py
- QuestionAnswerView: mascotMood = 'happy' for a correct answer else
'encourage', on both the fresh response and the idempotent-replay
response.
tests.py
- QuestionAnswerMascotMoodTests: correct -> happy, wrong -> encourage,
and the mood is preserved across an idempotent replay.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
When RAG is configured the endpoint retrieves lesson-scoped chunks and
generates a grounded answer; otherwise the previous deterministic stub
runs unchanged, so keyless runs and existing tests are unaffected.
Quota / idempotency / user-row lock / clean-503 behaviour is preserved on
every path.
models.py
- Conversation.subject (CharField) and Conversation.lesson_number
(IntegerField): retrieval metadata sent on the first ask and reused
for the rest of the conversation.
migrations/0004_conversation_lesson_number_conversation_subject.py
- migration for the two new fields.
serializers.py
- AskRequestSerializer: optional subject / grade / lessonNumber to pin
the Qdrant collection and lesson/chapter.
- AskResponseSerializer: usedChunks (retrieved [{title, summary}]) and
retrievalDebug (collection / filterKey / chapter-or-lesson / counts,
emitted only under DEBUG).
views.py
- generate_tutor_answer(): unchanged signature; RAG-configured branch
calls rag.services generation, stub branch otherwise.
- AskAIView.post(): resolve subject/grade/lessonNumber (falling back to
the Conversation), check the answer cache via resolve_target(), run
retrieve_context + generate_answer on a miss and populate the cache,
return usedChunks (+ retrievalDebug under DEBUG). Failures still
release the reserved quota slot and surface as 503.
- AskAIView._streaming_response(): opt-in "stream": true returns a
text/event-stream of chunk / done / error events; same retrieval,
phase-3 persist, idempotency and quota accounting as the JSON path,
and it consults the answer cache first. Non-stream JSON contract is
untouched.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
settings.py
- load .env via python-dotenv (override=False) so local runs and the RAG
keys work without `export`; production/docker env still wins.
- add 'rag' to INSTALLED_APPS.
- new RAG / AI provider block: OPENAI_API_KEY / OPENAI_BASE_URL (GapGPT)
/ OPENAI_CHAT_MODEL / OPENAI_VISION_MODEL / OPENAI_EMBEDDING_MODEL /
OPENAI_TIMEOUT_SECONDS, QDRANT_URL / QDRANT_API_KEY,
RAG_COLLECTION_NAME_TEMPLATE / RAG_GRADE_TITLE_OVERRIDES /
RAG_RETRIEVAL_TOP_K / RAG_FORCE_MOCK / RAG_ANSWER_CACHE_TTL.
- add 'rag_ingest' to DEFAULT_THROTTLE_RATES.
- under `manage.py test`: force RAG_FORCE_MOCK on and RAG_ANSWER_CACHE_TTL
to 0 so no test hits OpenAI/Qdrant or a warm cache.
urls.py
- mount rag.urls under /api/.
requirements.txt
- python-dotenv; openai, qdrant-client; pymupdf / pdf2image / pillow
(optional, only for PDF ingestion).
.env.example
- document the OPENAI_* / QDRANT_* / RAG_* keys, the discovered Qdrant
collection names, and the parked OPENAI_API_KEY_OPENAI_DIRECT slot
(no provider-switch logic by design).
Co-Authored-By: Claude Sonnet 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>
