How to Use the RAG Text Chunking & Overlap Visualizer
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1
Paste your document or load a sample technical or legal text passage.
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2
Adjust the chunk size slider (100 to 1,000 characters) and overlap slider (0% to 50%).
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3
Inspect the live rendered cards showing each chunk segment with preserved word boundaries.
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4
Export the parsed chunk objects as clean JSON ready for ingestion into Pinecone or Chroma.
Practical Example & Use Case
Testing whether a 300-character chunk with 60-character (20%) overlap preserves semantic context across sentence boundaries in an enterprise customer knowledge base.
Technical Notes & Limitations
Calculates character-based window splitting with natural whitespace snapping. Processing executes 100% locally in browser memory.
Frequently Asked Questions
Why is chunk overlap necessary in RAG?
Overlap ensures that facts spanning across chunk boundaries are not severed mid-sentence. A 10% to 20% overlap guarantees adjacent chunks share semantic continuity for the embedding model.
What is the optimal chunk size for RAG?
For specific factual Q&A, smaller chunks (256–512 tokens / 500–1,000 characters) yield higher embedding precision. For high-level summarization, larger chunks (1,000–2,000 tokens) are preferred.
Can I export the chunks into my vector database?
Yes, click "Export Chunks JSON" to download an array of structured JSON objects with unique chunk IDs and character start/end coordinates.