docs/API_USAGE_LIST.md

API Usage Inventory

This document lists all external API integrations in the application.

1. NVIDIA NIM API

Purpose: OCR (Optical Character Recognition) for extracting text from images

API Key Required: NVIDIA_API_KEY

Endpoints:

• OCR: https://ai.api.nvidia.com/v1/cv/nvidia/nemoretriever-ocr-v1
• Chat/Parser: https://integrate.api.nvidia.com/v1/chat/completions

Used In:

• config.py - Configuration setup
• processing.py - call_nim_ocr_api() function for OCR
• routes.py - Question number extraction and redaction features
• redact.py - Picture redaction in images
• test.sh - Testing script
• templates/question_entry_v2.html - Frontend OCR feature

Features:

• Automatic question number extraction from cropped images
• Text detection and OCR processing
• Image redaction for removing pictures from scanned documents

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2. Google Gemini API

Purpose: AI-powered question classification and question-answer extraction

API Key Required: GEMINI_API_KEY or GOOGLE_API_KEY

Endpoints:

• https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-lite:generateContent

Used In:

• gemini_classifier.py - classify_questions_with_gemini() - Classifies questions into subjects and chapters
• gemini_subjective.py - Subjective question generation
• qtab_routes.py - process_image_for_questions() - Extracts question-answer pairs from images
• classifier_routes.py - Auto-classification of cropped questions (via user setting)
• neetprep.py - NeetPrep question classification (via user setting)

Features:

• Automatic question classification by subject and NCERT chapter
• Question-answer pair extraction from answer key images
• Subjective question generation
• Model: gemini-2.0-flash-lite

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3. OpenRouter API (Amazon Nova)

Purpose: Alternative AI model for question classification

API Key Required: OPENROUTER_API_KEY

Endpoints:

• https://openrouter.ai/api/v1/chat/completions

Used In:

• nova_classifier.py - classify_questions_with_nova() - Classifies questions using Amazon Nova model
• classifier_routes.py - Auto-classification (when user selects Nova model)
• neetprep.py - NeetPrep question classification (when user selects Nova model)
• test.py - Testing script for Nova API

Features:

• Question classification by subject and NCERT chapter
• Alternative to Gemini classifier
• Model: amazon/nova-2-lite-v1:free
• User-selectable via Settings page

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4. NeetPrep GraphQL API

Purpose: Fetch questions and test attempts from NeetPrep platform

API Key Required: None (uses session/headers)

Endpoints:

• https://www.neetprep.com/graphql

Used In:

• neetprep.py - run_hardcoded_query() function

Features:

• Fetch user test attempts
• Get incorrect question IDs
• Retrieve question details (text, options, correct answer, level, topics)
• Batch processing of questions

GraphQL Queries:

• GetAttempts - Fetch test attempts
• GetIncorrectIds - Get incorrect question IDs from attempts
• GetQuestionDetails - Retrieve full question data

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5. External Resource Downloads

Purpose: Download files from external sources

Used In:

• routes.py - Download PDFs and images from URLs
• cli.py - CLI download functionality
• utils.py - Download Arial font from GitHub
• json_processor.py / json_processor_v3.py - Download images from URLs for PDF generation

Endpoints (Examples):

• GitHub: https://github.com/kavin808/arial.ttf/raw/refs/heads/master/arial.ttf
• User-provided PDF/image URLs

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API Key Summary

Environment Variable	Required For	Used By
NVIDIA_API_KEY	NVIDIA NIM OCR	processing.py, routes.py, redact.py, config.py
GEMINI_API_KEY or GOOGLE_API_KEY	Google Gemini AI	gemini_classifier.py, gemini_subjective.py, qtab_routes.py
OPENROUTER_API_KEY	Amazon Nova via OpenRouter	nova_classifier.py, test.py

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User-Configurable API Settings

Classifier Model Selection

Location: Settings page (templates/settings.html)

Database Field: users.classifier_model

Options:

1. Gemini Classifier (Default) - Uses Google Gemini API
2. Amazon Nova Lite - Uses OpenRouter API

Affects:

• classifier_routes.py - Auto-classification of cropped questions
• neetprep.py - NeetPrep question classification

Users can choose their preferred AI model for question classification based on:

• API key availability
• Model performance preferences
• Cost considerations

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Rate Limiting & Timeouts

Configured Timeouts:

• NVIDIA NIM OCR: 300 seconds (5 minutes)
• Gemini API: 300 seconds (5 minutes)
• Nova API: 300 seconds (5 minutes)
• NeetPrep GraphQL: 30 seconds
• Font download: 30 seconds

Batch Processing:

• Classifier batch size: 7 questions per batch
• Wait time between batches:
  • Classifier routes: 5 seconds
  • NeetPrep: 6 seconds

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Notes

1. API Key Storage: All API keys are stored as environment variables, not in the database
2. Error Handling: All API calls include error handling with logging
3. Fallback Behavior: If APIs are unavailable, features gracefully disable with user notifications
4. Security: API keys are never exposed in templates or client-side code