Building PDF-Insight

Key Features

The application offers several powerful capabilities:

• Smart Document Analysis: Upload PDFs and get AI-powered summaries and insights
• Interactive Chat Interface: Ask questions about your documents and get contextual answers
• Document Comparison: Compare different versions of documents with AI-assisted diff analysis
• Real-time Processing: Stream responses for better user experience
• Offline Support: Built-in offline detection and handling

Technical Architecture

Frontend Framework

The application is built on Next.js 14, utilizing the App Router for improved routing and server components. The UI is crafted using Tailwind CSS with a custom theme configuration that supports both light and dark modes

Document Processing Pipeline

When a user uploads a PDF, the document goes through several processing stages:

• Chunking: The document is split into manageable chunks
• Embedding Generation: Each chunk is converted to embeddings using OpenAI's embedding model
• Vector Storage: Embeddings are stored in MongoDB for efficient similarity search

Vector Search Implementation

One of the most interesting technical challenges was implementing efficient vector search for document queries. The application uses MongoDB's vector search capabilities

Performance Optimizations

Several optimizations were implemented to ensure good performance:

• Chunk Batching: Document processing is done in batches to manage memory efficiently
• Response Streaming: AI responses are streamed for better user experience
• Caching: Results are cached with TTL to reduce API calls
• Bundle Optimization: Next.js config includes specific optimizations: