Zareian
ac93e17305
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>
56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
"""
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کلاینت‌های OpenAI و Qdrant به‌صورتِ lazy (نه در سطحِ ماژول).
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مخزنِ Rag_backend این‌ها رو موقعِ import می‌ساخت؛ نتیجه این بود که بدونِ
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کلید/شبکه، خودِ import هم crash می‌کرد. اینجا کلاینت فقط وقتی واقعاً لازم
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بشه ساخته می‌شه و بینِ فراخوانی‌ها cache می‌مونه.
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"""
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from functools import lru_cache
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from django.conf import settings
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class RagNotConfigured(RuntimeError):
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"""کلید/کتابخانه‌ی لازم برای مسیرِ RAG موجود نیست."""
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@lru_cache(maxsize=1)
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def get_openai_client():
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if not settings.OPENAI_API_KEY:
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raise RagNotConfigured('OPENAI_API_KEY تنظیم نشده است.')
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try:
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from openai import OpenAI
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except ModuleNotFoundError as exc:
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raise RagNotConfigured('پکیجِ openai نصب نیست (pip install -r requirements.txt).') from exc
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return OpenAI(
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base_url=settings.OPENAI_BASE_URL,
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api_key=settings.OPENAI_API_KEY,
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timeout=settings.OPENAI_TIMEOUT_SECONDS,
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)
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@lru_cache(maxsize=1)
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def get_qdrant_client():
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if not settings.QDRANT_API_KEY:
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raise RagNotConfigured('QDRANT_API_KEY تنظیم نشده است.')
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try:
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from qdrant_client import QdrantClient
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except ModuleNotFoundError as exc:
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raise RagNotConfigured('پکیجِ qdrant-client نصب نیست (pip install -r requirements.txt).') from exc
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# check_compatibility=False: کلاینتِ نصب‌شده (1.19) با سرورِ تیمِ دیگه
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# (1.17) اختلافِ minor داره و کلاینت warning می‌ده؛ APIهایی که استفاده
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# می‌کنیم (get_collections/upsert/query_points/scroll) پایدارن.
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return QdrantClient(
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url=settings.QDRANT_URL,
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api_key=settings.QDRANT_API_KEY,
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check_compatibility=False,
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)
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def reset_clients_cache():
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"""برای تست‌ها / بعد از تغییرِ settings در زمانِ اجرا."""
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get_openai_client.cache_clear()
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get_qdrant_client.cache_clear()
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