PDF Search Service

A high-performance Node.js application that enables intelligent search across unstructured PDF data including text paragraphs, images, and tables.

🏗️ Architecture Overview

Data Flow

PDF → Parser → ETL Pipeline → Search Index → Search API → Results

Core Components

• PDF Parser: Extracts paragraphs, images, and tables from PDF documents (not implemented, assuming we have this already)
• ETL Pipeline: Processes and transforms extracted data for search indexing
• Search Engine: Document-scoped search within individual PDFs
• REST API: Secure endpoints for document upload and search operations

🛠️ Technology Stack

Backend Technologies

• Node.js with Express.js framework
• MeiliSearch - Fast, typo-tolerant search engine
• Redis - Caching and rate limiting
• SQLite - Document metadata storage
• Bull Queue - Async job processing

Text Processing & AI

• Tesseract.js - OCR for image text extraction
• Natural - Text processing and normalization
• Joi - Input validation

Security

• API key authentication
• Rate limiting with express-rate-limit
• CORS configuration
• Helmet.js security headers

🌐 Cloud Services Required

Core Infrastructure

• Compute Service: AWS EC2 For running your Node.js application, I would use an AWS EC2 instance. With increasing traffic we can use a nginx reverse proxy setup for laod distribution With very high traffic we can move on to use AWS Application Load Balancer (ALB).
• PDF File Storage: AWS S3 For storing original PDF files, I would use a Object storage service like AWS S3, which would be cost effective and give us built-in CDN integration for faster global access to PDFs.
• Redis: AWS ElastiCache We can run a self managed Redis cluster on the EC2 instances initially. With high traffic we can move to a managed redis service like AWS ElastiCache.

🚀 Key Features

Search Capabilities

• Full-text search across paragraphs, images (OCR), and tables
• Typo-tolerant search with fuzzy matching
• Content filtering by type (paragraph/image/table)
• Fast response times with document-scoped indexing

Processing Features

• Async processing with job queues
• OCR integration for image text extraction
• Table content flattening for searchability

Performance & Scalability

• Single document focus for optimized performance
• Efficient indexing with MeiliSearch
• Redis caching for frequently accessed data
• Rate limiting to prevent abuse

🔒 Security Features

• API Key Authentication - Secure access control
• Rate Limiting - Prevents API abuse
• Input Validation - Sanitizes all inputs
• CORS Protection - Configurable cross-origin policies
• Security Headers - HTTP security best practices