Zareian
42783f9ba4
Tutor (prompt tutor-v3-stepwise-plain): plain text instead of LaTeX/markdown, with rag/services/mathtext.py converting whatever still slips through; full step-by-step solutions with a check before the final answer; three tones by grade; the last 6 messages of the conversation go to the model and follow-ups are no longer served from another user's cache; book solutions the checker disputes are kept out of the context; a questionId gives the tutor the practice question and the student's wrong answer. /api/health reports when the AI credit runs out. rag_model_benchmark compares chat models on verified book problems (run on the server; credit ran out mid-run). Map: only lessons that have questions count toward finishing an island, lessonIds[0] / nextLessonId is always the next lesson to play, and a finished island stays finished. The Seyar characters (sigma/delta/pi) are seeded. Audit fixes, each reproduced before fixing: - the plan is stored on the order, so paying a monthly order after starting a yearly checkout no longer grants a year - opening checkout no longer demotes an active subscription; renewals extend from the current expiry; expired subscriptions are not reported active - one discount code per order and only before the gateway; a 100% code completes the purchase; lost Zarinpal confirmations are re-verified - the unused /api/payment/webhook "mark paid" endpoint is removed - XP counts only answers recorded on the server - 6-digit OTP, each code dies after 5 wrong guesses; POST /api/auth/logout - a guest who signs up keeps their account and progress (test phones excluded) - affiliate commission on the first real payment; guests excluded - parents with several children no longer get a 500 - SQLite runs transactions IMMEDIATE, since it ignores select_for_update - admin login is rate-limited; display names drop phone numbers and links; userQuestion is capped at 2000 characters - server-side heart enforcement exists behind HEARTS_ENFORCED (off until the frontend handles 403 no_hearts) Not done on purpose: rotating the server's secret key and moving to Postgres. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
66 lines
3.7 KiB
Python
66 lines
3.7 KiB
Python
from rest_framework import serializers
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class AskRequestSerializer(serializers.Serializer):
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conversationId = serializers.CharField(required=False, allow_null=True, allow_blank=True)
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lessonId = serializers.CharField(required=False, allow_null=True, allow_blank=True)
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chapterId = serializers.CharField(required=False, allow_null=True, allow_blank=True)
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gradeId = serializers.IntegerField(required=False, allow_null=True)
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questionId = serializers.CharField(required=False, allow_null=True, allow_blank=True)
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# metadataِ retrieval برای لایه‌ی RAG (اپ rag): برای زدن به دیتای واقعیِ
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# کتاب‌ها در Qdrant باید subject (مثلاً «علوم تجربی») + gradeId + lessonNumber
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# همگی مطابقِ دادهٔ موجود ارسال بشن؛ اگه نباشن، پاسخ به‌صورتِ تدریسِ عمومی
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# (بدونِ استناد به کتاب) تولید می‌شه. در ادامه‌ی همون مکالمه لازم نیست
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# دوباره فرستاده بشن (روی Conversation ذخیره می‌شن).
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subject = serializers.CharField(required=False, allow_null=True, allow_blank=True)
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# جزءِ «پایه» در نامِ collectionِ Qdrant (مثلاً «ششم»). اگه بیاد مستقیم
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# استفاده می‌شه؛ اگه نه، از روی gradeId و content.Grade.title / نگاشتِ
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# RAG_GRADE_TITLE_OVERRIDES ساخته می‌شه.
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grade = serializers.CharField(required=False, allow_null=True, allow_blank=True)
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lessonNumber = serializers.IntegerField(required=False, allow_null=True)
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# سقفِ طول: کلِ متن به مدلِ پولی می‌رود؛ بی‌سقف، یک درخواستِ صدهزار نویسه‌ای
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# به‌تنهایی هزینه‌ی صدها سوالِ عادی را داشت.
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userQuestion = serializers.CharField(max_length=2000)
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stream = serializers.BooleanField(required=False, default=False)
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class UsedChunkSerializer(serializers.Serializer):
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title = serializers.CharField(allow_blank=True)
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summary = serializers.CharField()
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class AskResponseSerializer(serializers.Serializer):
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conversationId = serializers.CharField()
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messageId = serializers.CharField()
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answer = serializers.CharField()
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suggestedFollowUps = serializers.ListField(child=serializers.CharField())
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remainingQuota = serializers.IntegerField()
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# تکه‌های درسیِ بازیابی‌شده از Qdrant که پاسخ بر پایه‌شون ساخته شده؛
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# خالی یعنی retrieval انجام نشده (نبودِ subject/lessonNumber یا خاموش‌بودنِ RAG).
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usedChunks = UsedChunkSerializer(many=True, required=False, default=list)
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# فقط وقتی DEBUG=True: از کدام collection/فصل چانک‌ها برداشته شد (بازبینیِ داخلی).
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retrievalDebug = serializers.DictField(required=False)
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class MessageSerializer(serializers.Serializer):
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messageId = serializers.CharField()
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role = serializers.CharField()
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text = serializers.CharField()
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class ConversationMessagesResponseSerializer(serializers.Serializer):
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conversationId = serializers.CharField()
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lessonId = serializers.CharField(allow_null=True)
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messages = MessageSerializer(many=True)
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class FeedbackRequestSerializer(serializers.Serializer):
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isHelpful = serializers.BooleanField()
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# اختیاری: «چرا». تنها چیزی که از خودِ 👍/👎 درنمی‌آید و برای اصلاحِ
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# پرامپت واقعاً به کار می‌آید.
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reason = serializers.CharField(max_length=300, required=False, allow_blank=True)
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class FeedbackResponseSerializer(serializers.Serializer):
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success = serializers.BooleanField()
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