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/models.py

@@ -0,0 +1,63 @@
+import uuid
+
+from django.db import models
+
+
+class LessonChunk(models.Model):
+    """
+    یک تکه‌ی درسیِ خلاصه‌شده که هم در همین جدول (منبعِ حقیقت) و هم به‌صورتِ
+    یک نقطه در Qdrant (فقط index، قابلِ بازسازی) ذخیره می‌شه.
+
+    برداشتِ پاک‌سازی‌شده از subjects.LessonChunk مخزنِ Rag_backend، با این
+    تفاوت‌ها طبق معماریِ این پروژه:
+    - هیچ FK بین‌اپی نداره؛ lesson_id فقط یک ارجاعِ سبک به content.Lesson است
+      (دقیقاً مثل Conversation.lesson_id در ai_tutor).
+    - subject/grade به‌صورتِ رشته/عدد نگه‌داری می‌شن چون مدلِ content ما مدلِ
+      Subject نداره و نامِ collectionِ تیمِ دیگه بر پایه‌ی همین رشته‌هاست.
+    - embedding_vector هم ذخیره می‌شه تا collectionِ Qdrant بدونِ فراخوانیِ
+      دوباره‌ی embedding قابلِ بازسازی باشه.
+    """
+
+    lesson_id = models.IntegerField(db_index=True)          # ارجاع سبک به content.Lesson
+    grade_id = models.IntegerField(null=True, blank=True)   # ارجاع سبک به content.Grade
+    subject = models.CharField(max_length=100)
+    lesson_number = models.IntegerField()
+    chapter = models.IntegerField(null=True, blank=True)
+
+    order = models.PositiveIntegerField()
+    title = models.CharField(max_length=255)
+    original_text = models.TextField()
+    summary = models.TextField()
+    embedding_vector = models.JSONField(null=True, blank=True)
+
+    collection = models.CharField(max_length=200, db_index=True)
+    qdrant_point_id = models.UUIDField(default=uuid.uuid4, editable=False)
+
+    created_at = models.DateTimeField(auto_now_add=True)
+
+    class Meta:
+        ordering = ['lesson_id', 'order']
+        constraints = [
+            models.UniqueConstraint(
+                fields=['collection', 'qdrant_point_id'],
+                name='uniq_lesson_chunk_qdrant_point',
+            ),
+        ]
+        indexes = [
+            models.Index(fields=['subject', 'lesson_number']),
+        ]
+
+    def __str__(self):
+        return f"chunk-lesson{self.lesson_id}-{self.order} ({self.subject})"
+
+
+class LessonChunkImage(models.Model):
+    """تصویرِ پیوست به یک chunk (برای محتوای چندوجهیِ آینده)."""
+
+    chunk = models.ForeignKey(LessonChunk, on_delete=models.CASCADE, related_name='images')
+    img_url = models.TextField()
+    description = models.TextField(blank=True)
+    created_at = models.DateTimeField(auto_now_add=True)
+
+    def __str__(self):
+        return f"image-chunk{self.chunk_id}"