Chunk Visualizer

What is Chunk Visualizer ?

Chunk Visualizer is a tool designed to split text into manageable chunks and visualize these segments. This application is particularly useful for Retrieval-Augmented Generation (RAG) systems, where understanding text chunks with overlap is essential. It helps users identify how text can be divided, analyze overlapping sections, and process information more effectively.

Features

• Text Chunking: Split text into smaller segments based on predefined or custom rules.
• Adjustable Overlap: Define the level of overlap between consecutive chunks for better context retention.
• Visual Representation: Gain insights through clear visualizations of how your text is divided.
• Compatibility: Works seamlessly with various text generation models and RAG systems.
• Customization Options: Tweak settings to suit your specific use case and analysis needs.

How to use Chunk Visualizer ?

1. Input Your Text: Start by entering or uploading the text you want to analyze.
2. Choose a Chunking Method: Select a predefined or custom chunking method based on your requirements.
3. Adjust Overlap Settings: Set the desired level of overlap between chunks to capture context effectively.
4. Process the Text: Run the tool to split the text into chunks.
5. Visualize the Chunks: Review the visualized chunks and their overlaps.
6. Export Results: Save or export the chunk data for further analysis or integration into other systems.

Frequently Asked Questions

What is the purpose of overlapping chunks?
Overlapping chunks are used to maintain context between consecutive segments, which is especially important for RAG systems to understand relationships between different parts of the text.

Can I customize how the text is split?
Yes, Chunk Visualizer allows you to define custom rules for splitting text, ensuring flexibility for different use cases and applications.

What applications are supported by Chunk Visualizer for RAG?
Chunk Visualizer is designed to work with most RAG systems and is compatible with popular text generation models, making it versatile for a wide range of applications.