RAG Document Chunking Simulator
Simulate document chunking by character or token count with configurable sliding window overlap.
100% Client-Side · In-Memory Only
Interactive Tool Workspace
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How to Use RAG Document Chunking Simulator
- Paste your text or upload a document file into the source editor.
- Configure your desired chunk size and sliding window overlap in characters or words.
- Examine the generated chunk cards with individual character, word, and estimated token counts.
- Click Export JSON Chunks to download structured JSON arrays for vector database ingestion.
Features & Guarantees
- Visualizes document partitioning for Retrieval-Augmented Generation (RAG) pipelines.
- Configurable sliding window overlap to prevent losing context across chunk boundaries.
- Supports character-based and word-based splitting units.
- One-click JSON export ready for embedding models and vector databases like Pinecone, Milvus, or pgvector.
Frequently Asked Questions
What is sliding window chunk overlap?
Overlap repeats the last N characters or words of chunk K at the start of chunk K+1, ensuring sentences or thoughts split across boundaries retain full contextual meaning in vector embeddings.
What is an ideal chunk size for RAG?
Common RAG chunk sizes range between 200 to 500 tokens (800-2000 characters) with 10-20% overlap, balancing semantic specificity with surrounding context.