Zareian 5416fbb2ba real RAG answers on POST /api/ai/ask, plus SSE streaming and answer cache ...

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>

2026-08-29 02:30:33 +03:30

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