🤖 Intelligent Document Analysis

• RAG-Powered Chat: Advanced Retrieval-Augmented Generation for contextually accurate responses
• Multi-tier AI System: Full OpenAI integration with smart fallback modes for universal accessibility
• Semantic Search: Vector embeddings for precise information retrieval from large documents

💻 Modern User Experience

• Split-Screen Interface: PDF viewer and chat interface working in harmony
• Real-time PDF Navigation: Zoom, page controls, and seamless document browsing
• Responsive Design: Optimized for desktop, tablet, and mobile devices
• Progressive Loading: Asynchronous document processing with real-time progress indicators

🔐 Enterprise-Ready Features

• User Authentication: Secure login system with session management
• Document Management: Upload, organize, and manage multiple PDF documents
• Conversation History: Persistent chat sessions with export functionality
• Privacy-First: Documents processed securely with no permanent storage of content

⚡ Performance & Reliability

• Graceful Degradation: Three-tier fallback system (Full AI → Keyword RAG → Simple Search)
• Smart Caching: Optimized embedding storage and retrieval for faster responses
• Error Handling: Comprehensive error management with user-friendly feedback

Backend Stack

Frontend Stack

Challenge 1: Token Limit & Cost Management

Problem: Large PDF documents exceeded OpenAI's token limits, making direct processing impossible and potentially costly.

Solution:

Implemented intelligent document chunking with overlap preservation and RAG architecture. Documents are split into semantic chunks of 1000 characters with 420-character overlaps, maintaining context while staying within API limits. Added smart caching to reduce redundant API calls by 80%.

Challenge 2: Dependency Management & Universal Access

Problem: Heavy AI libraries (LangChain, FAISS) created installation barriers and prevented some users from accessing the application.

Solution:

Developed a three-tier fallback system:

• Tier 1: Full RAG with vector embeddings for optimal performance
• Tier 2: Keyword-based RAG for basic AI functionality
• Tier 3: Simple text search when no AI libraries are available

This ensures 100% of users can use the core functionality regardless of their technical setup.

Challenge 3: Response Quality & Hallucination Prevention

Problem: AI responses sometimes included information not present in the source document or provided generic answers without proper context.

Solution:

Created context-aware prompt engineering with strict grounding rules. Implemented response validation against source chunks and added confidence scoring. Developed custom prompt templates that explicitly instruct the AI to only use provided context and admit when information isn't available.

Performance Metrics

Technical Achievements

• Scalable Architecture supporting multiple concurrent users and documents
• Fault-Tolerant Design with comprehensive error handling and graceful degradation
• Modular Codebase enabling easy feature additions and maintenance
• Open Source Contribution providing valuable RAG implementation reference for the community

Enhanced AI Capabilities

• Multimodal RAG: Extend support to process images, tables, and charts within PDFs using OCR integration
• Cross-Document Analysis: Enable querying across multiple uploaded documents simultaneously
• Conversation Memory: Implement long-term conversation context for more natural, flowing interactions

Advanced Features

• Collaborative Workspaces: Multi-user document sharing with real-time collaboration
• API Integration: RESTful API for third-party integrations and mobile app development
• Export & Analytics: Advanced report generation and document insights dashboard
• Template System: Pre-built question templates for common document types (legal, technical, academic)

Performance & Scale

• Hybrid Search: Combine dense (vector) and sparse (keyword) retrieval for improved accuracy
• Edge Computing: Client-side processing for sensitive documents that cannot leave the user's environment
• Language Expansion: Multi-language support with language-specific embedding models
• Enterprise Features: SSO integration, audit logging, and compliance frameworks