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>
