RAG Chunking / Text Splitter Tool

Split long documents into optimized chunks for RAG (Retrieval-Augmented Generation) ingestion. Perfect for building chatbots on PDFs, document Q&A systems, and vector database preparation. Configure chunk size, overlap, and export as JSON, Markdown, or plain text. All processing happens in your browser - no backend required.

RAG Chunking / Text Splitter Tool

Split long documents into smaller chunks optimized for RAG (Retrieval-Augmented Generation) ingestion. Perfect for building chatbots on PDFs, document Q&A systems, and vector database preparation. Export chunks as JSON, Markdown, or plain text.

Paste your document text:

Characters: 0 | Estimated Tokens: ~0

Chunking Configuration

Chunk Size

Recommended: 500-1000 characters or 150-300 tokens

Overlap (Sliding Window)

Overlap helps preserve context. 10-20% of chunk size recommended

💡 RAG Chunking Best Practices

Chunk Size: 500-1000 characters (150-300 tokens) works well for most LLMs. Larger chunks (1500-2000 chars) for longer context models.
Overlap: 10-20% overlap helps preserve context and prevents splitting important sentences in the middle. Essential for maintaining semantic meaning.
Token Estimation: Roughly 4 characters per token (varies by language). Use actual tokenizers (tiktoken, etc.) for production accuracy.
Sentence Boundaries: For better quality, consider splitting at sentence or paragraph boundaries rather than fixed sizes (future enhancement).
Metadata: Include chunk index, source document info, and character positions for better retrieval tracking.

What is RAG Chunking?

RAG (Retrieval-Augmented Generation) requires splitting large documents into smaller, manageable chunks that can be embedded into vectors and retrieved during question-answering. Effective chunking is crucial for building accurate document Q&A systems, chatbots, and knowledge bases powered by LLMs.

Why Use This Tool?

🎯 Perfect Chunk Sizes

Optimize chunk sizes for your LLM's context window. Balance between too small (loses context) and too large (inefficient retrieval).

🔄 Context Overlap

Sliding window overlap ensures important context isn't lost at chunk boundaries. Critical for maintaining semantic meaning.

📤 Export Formats

Export as JSON for programmatic use, Markdown for documentation, or plain text for simple integrations.

⚡ No Backend Required

All processing happens in your browser. Your documents never leave your device. Perfect for sensitive or confidential content.

Use Cases

📚 Document Q&A Systems

Build chatbots that answer questions about PDFs, research papers, documentation, or legal documents. Chunk documents for vector database ingestion.

💼 Knowledge Bases

Create searchable knowledge bases from company documentation, wikis, or training materials. Enable semantic search over large text corpora.

🤖 AI Agents

Prepare documents for RAG-powered AI agents that need context from large document sets. Essential for agentic workflows and autonomous systems.

🔍 Vector Database Preparation

Prepare text chunks for embedding generation and storage in vector databases like Pinecone, Weaviate, Qdrant, or Chroma. Optimize for retrieval accuracy.

Technical Details

Token Estimation

Uses rough estimation of ~4 characters per token (varies by language and content). For production, use actual tokenizers like tiktoken(OpenAI), sentencepiece (Google), ortransformers (HuggingFace) for accuracy.

Export Format: JSON

JSON export includes metadata (chunk size, overlap, timestamps) and array of chunks with indices, text, character counts, and token estimates. Perfect for programmatic processing and API integration.

Export Format: Markdown

Human-readable markdown format with headers, metadata table, and formatted chunk content. Great for documentation, reviews, or manual inspection of chunks.

Privacy & Security

All processing occurs client-side in your browser using JavaScript and the Blob API. No data is sent to servers. Your documents remain private and secure.

Next Steps After Chunking

Generate Embeddings: Use embedding models (OpenAI, Cohere, Sentence-BERT) to convert chunks into vectors.
Store in Vector DB: Upload vectors to Pinecone, Weaviate, Qdrant, or similar vector databases.
Build Retrieval: Implement semantic search to find relevant chunks for user queries.
RAG Pipeline: Combine retrieved chunks with LLM prompts for context-aware responses.