📝 Input Text
Paste long text for chunking, optimized for RAG retrieval-augmented generation systems.
⚙️ Chunking Parameters
📖 AI Text Chunking Guide
What is Text Chunking?
Text chunking is a core preprocessing step in RAG (Retrieval-Augmented Generation) systems. Long documents are split into appropriately sized segments, each converted to a vector embedding and stored in a vector database for AI model retrieval.
Chunking Strategy Comparison
- Fixed Size: Split by character/token count, simple and efficient, but may cut mid-sentence
- By Sentence: Split at sentence boundaries, maintains semantic completeness, good for short texts
- By Paragraph: Split at paragraphs, preserves topic coherence, good for structured documents
- Recursive: Recursively split by separator hierarchy (paragraph→sentence→character), balances semantics and size
Best Practices
- Chunk size of 256-512 tokens balances retrieval precision and context completeness
- Set 10-20% overlap to prevent key information from being cut at boundaries
- Add metadata (source, position, title) to each chunk to improve retrieval quality
- For structured documents (HTML/Markdown), prefer heading-level chunking
- Use different strategies for different document types: recursive for technical docs, sentence for conversations
❓ FAQ
What is AI text chunking?
AI text chunking is the process of splitting long text into smaller segments for RAG (Retrieval-Augmented Generation) systems. Chunked text segments are converted to vector embeddings and stored in a vector database for AI models to retrieve relevant content.
What are common chunking strategies?
Common strategies include: 1) Fixed-size chunking - split by character or token count; 2) Sentence chunking - split at sentence boundaries; 3) Paragraph chunking - split at paragraphs; 4) Recursive chunking - recursively split by separator hierarchy.
Why do chunks need overlap?
Overlap ensures adjacent chunks share content, preventing key information from being cut at chunk boundaries. Typically 10-20% overlap rate balances information completeness and storage efficiency.
What chunk size should I use?
Chunk size depends on embedding model limits and application needs. OpenAI text-embedding-3-small max is 8191 tokens, typically suggesting 256-512 token chunks.
Is my data uploaded to a server?
No. All text chunking is processed locally in your browser. No data is sent to any server.