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/management/commands/rag_catalog.py

@@ -0,0 +1,77 @@
+"""
+فهرستِ زنده‌ی collectionهای Qdrant + تعدادِ نقطه و مقادیرِ فیلترِ درس/فصل.
+
+فقط خواندنی؛ هیچ‌چیزی روی Qdrant نمی‌نویسد. برای این‌که همیشه بدونیم چه
+کتاب/درسی روی سرورِ تیمِ دیگه در دسترس است و rag/services/known_collections.py
+با واقعیت هم‌خوان است.
+
+    python manage.py rag_catalog
+    python manage.py rag_catalog --values        # مقادیرِ lesson_number/chapter را هم چاپ کن
+"""
+import json
+
+from django.core.management.base import BaseCommand
+
+
+class Command(BaseCommand):
+    help = 'فهرستِ زنده‌ی collectionهای Qdrant (فقط خواندنی).'
+
+    def add_arguments(self, parser):
+        parser.add_argument('--values', action='store_true',
+                            help='مقادیرِ متمایزِ lesson_number/chapter هر collection را هم چاپ کن.')
+        parser.add_argument('--json', action='store_true', help='خروجی JSON.')
+
+    def handle(self, *args, **opts):
+        from rag.services.clients import RagNotConfigured, get_qdrant_client
+
+        try:
+            client = get_qdrant_client()
+            names = sorted(c.name for c in client.get_collections().collections)
+        except RagNotConfigured as exc:
+            self.stderr.write(self.style.ERROR(f'RAG پیکربندی نشده: {exc}'))
+            return
+        except Exception as exc:  # noqa: BLE001
+            self.stderr.write(self.style.ERROR(f'اتصال به Qdrant شکست خورد: {exc}'))
+            return
+
+        catalog = []
+        for name in names:
+            info = client.get_collection(name)
+            row = {
+                'collection': name,
+                'points': info.points_count,
+                'vectorSize': info.config.params.vectors.size,
+            }
+            if opts['values']:
+                lessons, chapters = set(), set()
+                offset = None
+                while True:
+                    pts, offset = client.scroll(collection_name=name, limit=256,
+                                                with_payload=True, with_vectors=False, offset=offset)
+                    for p in pts:
+                        pl = p.payload or {}
+                        if 'lesson_number' in pl:
+                            lessons.add(pl['lesson_number'])
+                        if 'chapter' in pl:
+                            chapters.add(pl['chapter'])
+                    if offset is None:
+                        break
+                if lessons:
+                    row['lessonNumbers'] = sorted(lessons, key=lambda x: (isinstance(x, str), x))
+                if chapters:
+                    row['chapters'] = sorted(chapters)
+            catalog.append(row)
+
+        if opts['json']:
+            self.stdout.write(json.dumps(catalog, ensure_ascii=False, indent=2))
+            return
+
+        total_points = sum(r['points'] for r in catalog)
+        self.stdout.write(self.style.SUCCESS(f'{len(catalog)} collection، مجموعاً {total_points} نقطه:\n'))
+        for r in catalog:
+            self.stdout.write(f"  • {r['collection']}  —  {r['points']} نقطه  (بُعد {r['vectorSize']})")
+            if 'lessonNumbers' in r:
+                self.stdout.write(f"      lesson_number: {r['lessonNumbers']}")
+            if 'chapters' in r:
+                for ch in r['chapters']:
+                    self.stdout.write(f"      chapter: {ch!r}")