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>
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
"""
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تقسیمِ متنِ درس به تکه‌های مستقل + خلاصه‌سازیِ آموزشیِ هر تکه.
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برداشتِ پاک‌سازی‌شده از subjects/services/chunker.py مخزنِ Rag_backend:
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- به‌جای HttpError نینجا، از استثناهای معمولی استفاده می‌کنیم (لایه‌ی view
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در DRF تبدیلش می‌کنه).
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- کلاینت lazy است.
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"""
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import json
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from django.conf import settings
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from . import clients
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class ChunkingError(RuntimeError):
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pass
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def split_text_into_chunks(text: str, chunk_size: int = 400, overlap: int = 100) -> list[str]:
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if not text:
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return []
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chunks: list[str] = []
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start = 0
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text_length = len(text)
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step = max(chunk_size - overlap, 1)
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while start < text_length:
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end = min(start + chunk_size, text_length)
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chunks.append(text[start:end])
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start += step
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return chunks
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def _strip_code_fence(raw: str) -> str:
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raw = raw.strip()
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if raw.startswith('```json'):
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raw = raw[len('```json'):]
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elif raw.startswith('```'):
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raw = raw[len('```'):]
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if raw.endswith('```'):
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raw = raw[:-3]
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return raw.strip()
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def summarize_chunk(chunk_text: str) -> str:
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"""خلاصه‌ی مستقل و آموزشیِ یک تکه به فارسی."""
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prompt = f"""
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You are an expert Persian educational summarizer.
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Summarize the following text clearly and independently.
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- The summary must be self-contained.
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- Capture all key educational points.
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- Write in natural Persian (Farsi).
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Text:
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{chunk_text}
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Return JSON only:
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{{"summary": "خلاصه کامل این بخش در فارسی"}}
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"""
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client = clients.get_openai_client()
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response = client.chat.completions.create(
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model=settings.OPENAI_CHAT_MODEL,
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messages=[
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{'role': 'system', 'content': 'You are a helpful assistant that always outputs valid JSON.'},
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{'role': 'user', 'content': prompt},
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],
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max_tokens=400,
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temperature=0.4,
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top_p=0.9,
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)
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raw = _strip_code_fence(response.choices[0].message.content or '')
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try:
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data = json.loads(raw)
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except json.JSONDecodeError as exc:
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raise ChunkingError(f'خروجیِ خلاصه‌سازی JSON معتبر نبود: {raw[:300]}') from exc
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summary = data.get('summary')
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if not summary:
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raise ChunkingError(f'کلیدِ summary در خروجی نبود: {raw[:300]}')
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return summary
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def chunk_and_summarize(text: str, chunk_size: int = 400, overlap: int = 100) -> list[dict]:
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"""[{order, original_text, summary}, ...]"""
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result = []
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for index, original in enumerate(split_text_into_chunks(text, chunk_size, overlap), start=1):
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result.append({
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'order': index,
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'original_text': original,
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'summary': summarize_chunk(original),
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})
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return result
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