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>
