Math Textbook Q&A System
RAG-powered Q&A platform for 100-page math textbooks with grounded answers, step-by-step solving, page citations, and intelligent tutor mode
Feature Overview
Comprehensive math textbook Q&A platform with RAG-powered intelligence and step-by-step tutoring
Core Features
- Upload and store 100-page math textbook PDFs
- Extract text with page-level metadata
- Math-specific chunking by section boundaries
- OpenAI embeddings with vector indexing
- Semantic retrieval with optional keyword search
- Grounded answers with page citations
- Step-by-step tutor mode for problem solving
- Multi-turn chat sessions with history
Advanced Features
- Hybrid retrieval: vector similarity + BM25
- Diversity constraint to avoid duplicate chunks
- LaTeX formula extraction and rendering
- OCR fallback for scanned textbooks
- Confidence scoring for answer quality
- Idempotent ingestion with file/chunk hashing
- Real-time ingestion status tracking
- Async worker processing with job queues
System Architecture
Modern, scalable architecture with async ingestion, hybrid retrieval, and chat-based Q&A
React Frontend
- •PDF upload with drag-and-drop
- •Real-time ingestion progress
- •Q&A interface with chat history
- •Citation viewer with page navigation
- •Tutor mode toggle and step display
FastAPI Backend
- •RESTful API endpoints
- •PDF storage orchestration
- •Worker job queue management
- •Retrieval pipeline integration
- •OpenAI chat completion proxy
Ingestion Worker
- •PDF text extraction (PyPDF2/pdfplumber)
- •Math-specific chunking strategy
- •Section/heading detection
- •Batch embedding generation
- •Vector store upsert operations
Storage Layers
- •S3/Blob for PDF files
- •PostgreSQL for metadata
- •Qdrant/pgvector for embeddings
- •Redis for job queue
- •Optional Elasticsearch for keywords
RAG Pipeline
End-to-end pipeline from PDF upload to grounded answers with citations
PDF Parsing
Extract text per page, identify sections, headings, examples, and exercises
Math-Specific Chunking
Chunk by semantic boundaries (400-900 tokens) with metadata enrichment
Embedding Generation
Batch OpenAI embeddings for chunks with retry and cost optimization
Vector Indexing
Upsert to Qdrant/pgvector with doc_id, pages, and section paths
Hybrid Retrieval
Vector similarity + optional BM25 keyword search with diversity
Grounded Answer
OpenAI chat with context, citations, and optional step-by-step mode
Answer Modes & Chat
Flexible Q&A modes for different learning styles with persistent chat sessions
Grounded Mode
Answers strictly based on textbook content. Refuses to answer if information is not found in the book. Perfect for learning from the source material.
Tutor Mode
Combines textbook grounding with step-by-step problem solving. Breaks down solutions while citing relevant textbook rules and definitions.
Chat Sessions
Multi-turn conversations with persistent history. Follow-up questions reference previous context for natural dialogue flow.
Citations & Confidence
Every answer includes page ranges, section paths, and confidence scores. Click citations to view source chunks directly.
Key Benefits
Transform how students, tutors, and teachers interact with math textbooks
10× Faster Learning
Students find answers instantly instead of flipping through pages manually
Grounded & Accurate
All answers cite specific pages and sections, preventing hallucinations
Privacy-First
Documents stay within your infrastructure with no external data sharing
Scalable Architecture
Async workers handle multiple books simultaneously with auto-scaling
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