Text Chunk Visualizer

Visualize how your text will be chunked with different settings for RAG and semantic search.

Understanding Text Chunking

Text chunking is crucial for RAG systems. Smaller chunks provide more precise retrieval but may lose context, while larger chunks maintain context but may be less precise. Overlap helps maintain continuity between chunks.

Chunking Settings

Simulates how many chunks would be retrieved

Statistics

Total Chunks2
Words per Chunk~50
Overlap10 words
Top-K Chunks3

Generated Chunks (2)

Chunk 1 • Top-K50 words

Large Language Models (LLMs) are advanced AI systems trained on massive amounts of text data. They can understand and generate human-like text, making them useful for a wide range of applications. When working with LLMs, it's important to understand how text is processed and chunked. Chunking strategies can significantly impact

Chunk 2 • Top-K21 words

text is processed and chunked. Chunking strategies can significantly impact the quality of your results, especially in retrieval-augmented generation (RAG) systems.