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RAG Text Chunking & Overlap Visualizer
Visualize text splitting strategies, token/character chunk sizes, and overlaps for RAG vector indexing in your browser.
RAG Text Chunking & Overlap Visualizer: TOEA splits documents into text chunks locally using your chosen strategy. It calculates character ranges, token estimates (~4 chars/token), and explicit overlap text between consecutive chunks. Runs 100% locally in your browser with zero server file uploads.
- Category
- Developer tools
- Runs
- In your browser
- Cost
- Free · no sign-up
- Availability
- Ready to use
Generated Document Chunks & Overlaps
# Introduction to Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation enhances Large Language Models by grounding outputs on external knowledge bases. Instead of relying solely on parametric memory learned during training, RAG models fetch relevant text passages at inference time.
Retrieval-Augmented Generation enhances Large Language Models by grounding outputs on external knowledge bases. Instead of relying solely on parametric memory learned during training, RAG models fetch relevant text passages at inference time. ## Chunking Strategies
## Chunking Strategies Proper text chunking is critical for RAG accuracy. Fixed-size chunking splits documents into uniform character intervals. Sentence-based chunking preserves grammatical units. Markdown chunking splits along section headers. ## Vector Indexing
Proper text chunking is critical for RAG accuracy. Fixed-size chunking splits documents into uniform character intervals. Sentence-based chunking preserves grammatical units. Markdown chunking splits along section headers. ## Vector Indexing
## Vector Indexing Once chunks are generated, embedding models convert text chunks into dense vector representations. These vectors are indexed in vector databases like Pinecone, Qdrant, or PGVector for fast cosine similarity search.
Characters, tokens, and model limits
Chunk size here is in characters, and the token figure assumes about four characters per token, which holds for English prose. Code, numbers, and many other languages use more tokens per character, so leave headroom. Check the result against your embedding model's input limit: many BERT-style models stop at 512 tokens and typically drop the rest, while OpenAI's text-embedding-3 models accept 8,191. At the 2,000-character maximum a chunk is roughly 500 English tokens.
Reading the preview
Look for chunks that begin mid-thought, headings separated from the text they introduce, and tables or code blocks cut in two; each will be retrieved without the context that gives it meaning. With the sentence, paragraph, and Markdown strategies, a single unit longer than the chunk size is kept whole, so an oversized chunk usually points to a very long paragraph or section.
With those three strategies, overlap also backs up by whole sentences, paragraphs, or sections rather than an exact character count. Many pipelines also prepend the document title or section heading to each chunk before embedding it.
How to use it
- Paste your document or code into the editor.
- Choose a splitting strategy (Paragraph, Sentence, Markdown Headers, or Fixed size) and adjust target chunk size and overlap sliders.
- Inspect color-coded chunk boundaries, character/token metrics, and overlap windows.
Privacy & limitations
Your source document stays 100% inside your browser memory.
Related tools
Frequently asked questions
Which chunking strategy should I use for RAG?
Paragraph boundaries work best for general text; Markdown Headers for structured documentation; Sentence boundaries for high-precision Q&A.
Why is chunk overlap important?
Overlap prevents semantic loss across chunk boundaries, ensuring vector similarity searches capture context that spans split points.
Free tool · runs in your browser · no account required