new app — cleaned-up RAG pipeline (Qdrant retrieval + GPT), ported from Rag_backend

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>

rag/services/clients.py

@@ -0,0 +1,55 @@
+"""
+کلاینت‌های OpenAI و Qdrant به‌صورتِ lazy (نه در سطحِ ماژول).
+
+مخزنِ Rag_backend این‌ها رو موقعِ import می‌ساخت؛ نتیجه این بود که بدونِ
+کلید/شبکه، خودِ import هم crash می‌کرد. اینجا کلاینت فقط وقتی واقعاً لازم
+بشه ساخته می‌شه و بینِ فراخوانی‌ها cache می‌مونه.
+"""
+from functools import lru_cache
+
+from django.conf import settings
+
+
+class RagNotConfigured(RuntimeError):
+    """کلید/کتابخانه‌ی لازم برای مسیرِ RAG موجود نیست."""
+
+
+@lru_cache(maxsize=1)
+def get_openai_client():
+    if not settings.OPENAI_API_KEY:
+        raise RagNotConfigured('OPENAI_API_KEY تنظیم نشده است.')
+    try:
+        from openai import OpenAI
+    except ModuleNotFoundError as exc:
+        raise RagNotConfigured('پکیجِ openai نصب نیست (pip install -r requirements.txt).') from exc
+
+    return OpenAI(
+        base_url=settings.OPENAI_BASE_URL,
+        api_key=settings.OPENAI_API_KEY,
+        timeout=settings.OPENAI_TIMEOUT_SECONDS,
+    )
+
+
+@lru_cache(maxsize=1)
+def get_qdrant_client():
+    if not settings.QDRANT_API_KEY:
+        raise RagNotConfigured('QDRANT_API_KEY تنظیم نشده است.')
+    try:
+        from qdrant_client import QdrantClient
+    except ModuleNotFoundError as exc:
+        raise RagNotConfigured('پکیجِ qdrant-client نصب نیست (pip install -r requirements.txt).') from exc
+
+    # check_compatibility=False: کلاینتِ نصب‌شده (1.19) با سرورِ تیمِ دیگه
+    # (1.17) اختلافِ minor داره و کلاینت warning می‌ده؛ API‌هایی که استفاده
+    # می‌کنیم (get_collections/upsert/query_points/scroll) پایدارن.
+    return QdrantClient(
+        url=settings.QDRANT_URL,
+        api_key=settings.QDRANT_API_KEY,
+        check_compatibility=False,
+    )
+
+
+def reset_clients_cache():
+    """برای تست‌ها / بعد از تغییرِ settings در زمانِ اجرا."""
+    get_openai_client.cache_clear()
+    get_qdrant_client.cache_clear()