RAG Chunking Strategy Visualizer

A comprehensive web application for exploring and comparing different chunking strategies for Retrieval-Augmented Generation (RAG) systems. This tool helps developers and researchers optimize RAG performance by visualizing how different chunking approaches affect document processing.

Features

Core Functionality

• PDF Upload & Text Extraction: Upload PDF documents and extract text content for analysis
• Multiple Chunking Strategies: Explore 6 different chunking approaches:
  • Fixed-Size Chunking
  • Recursive Character Splitting
  • Sentence-Based Chunking
  • Paragraph-Based Chunking
  • Semantic Sliding Window
  • Fixed Overlap Chunking
• Strategy Explanations: Detailed explanations of each strategy's principles, best use cases, and trade-offs
• Chunk Visualization: Interactive visualization of chunks with metadata including size, overlap, and positioning

Technical Features

• Performance Optimized: Efficient processing of large PDF documents
• Responsive Design: Works seamlessly across desktop and mobile devices
• Real-time Analysis: Instant chunking results with comprehensive metadata
• Error Handling: Robust error handling for invalid files and processing issues

Chunking Strategies Explained

1. Fixed-Size Chunking

• Principle: Splits text into chunks of exactly the same character count
• Best For: Uniform processing requirements, predictable chunk sizes
• Trade-offs: May break semantic meaning mid-sentence or mid-word

2. Recursive Character Splitting

• Principle: Intelligently splits text while preserving sentence and paragraph boundaries
• Best For: Maintaining semantic coherence while controlling chunk size
• Trade-offs: More complex processing, variable chunk sizes

3. Sentence-Based Chunking

• Principle: Groups complete sentences together into chunks (3-8 sentences per chunk)
• Best For: Maintaining grammatical and semantic integrity
• Trade-offs: Highly variable chunk sizes, may create very large chunks

4. Paragraph-Based Chunking

• Principle: Uses paragraph breaks as natural chunk boundaries
• Best For: Documents with clear paragraph structure and topics
• Trade-offs: Very variable sizes, some chunks may be extremely large

5. Semantic Sliding Window

• Principle: Creates overlapping chunks to maintain context across boundaries
• Best For: Ensuring no information is lost at chunk boundaries
• Trade-offs: Increased storage requirements due to overlap

6. Fixed Overlap Chunking

• Principle: Large chunks with significant overlap for maximum context retention
• Best For: Complex documents where context is crucial
• Trade-offs: High redundancy, increased processing and storage costs

Getting Started

Prerequisites

• Node.js 18+
• npm or yarn

Installation

1. Clone the repository:

   git clone https://github.com/Pranav452/RAG-Chunking-Strategy.gitgit
   cd rag-chunking-visualizer

2. Install dependencies:

   npm install

3. Run the development server:

   npm run dev

4. Open http://localhost:3000 in your browser

Usage

1. Upload PDF: Click the upload area and select a PDF document
2. Select Strategy: Choose a chunking strategy from the dropdown menu
3. Apply Chunking: Click "Apply Chunking Strategy" to process the document
4. Analyze Results: Review the chunk visualization and metadata

Technical Architecture

Built With

• Next.js 14: React framework with App Router
• TypeScript: Type-safe development
• Tailwind CSS: Utility-first CSS framework
• Radix UI: Accessible component primitives
• Lucide React: Beautiful icons

Key Components

• app/page.tsx: Main application interface
• app/api/extract-pdf/route.ts: PDF text extraction API endpoint
• components/ui/*: Reusable UI components

Chunking Implementation

Each chunking strategy is implemented as a pure function that takes text input and returns structured chunk data with metadata. The implementations focus on:

• Semantic Preservation: Maintaining meaning across chunk boundaries
• Performance: Efficient processing of large documents
• Flexibility: Configurable parameters for different use cases

When to Use Each Strategy

Fixed-Size Chunking

• Simple documents with uniform structure
• When predictable chunk sizes are required
• Resource-constrained environments

Recursive Character Splitting

• General-purpose chunking for most documents
• When balancing semantic coherence with size control
• Mixed content types (paragraphs, lists, etc.)

Sentence-Based Chunking

• Question-answering systems
• Documents where sentence integrity is crucial
• Legal or academic documents

Paragraph-Based Chunking

• Well-structured documents with clear topics
• When preserving document hierarchy is important
• Blog posts, articles, reports

Semantic Sliding Window

• Complex technical documents
• When context preservation is critical
• Multi-step reasoning tasks

Fixed Overlap Chunking

• High-stakes applications where information loss is costly
• Documents with interconnected concepts
• Research and analysis tasks

Performance Metrics

The application provides several metrics to evaluate chunking effectiveness:

• Total Chunks: Number of chunks generated
• Average Size: Mean chunk size in characters
• Average Overlap: Mean overlap between adjacent chunks
• Coverage: Percentage of original text covered