Split text into chunks for RAG preprocessing
Yes, completely free. Chunk texts of any length with no restrictions on chunk count or processing volume.
No. All chunking is performed locally in your browser. Your documents remain private on your device.
Text chunking splits large documents into smaller, overlapping segments for processing by systems with context limits. Key use cases: (1) RAG (Retrieval-Augmented Generation) — chunk documents into 512–1024 token segments with 10–20% overlap for embedding into vector databases. Chunks become searchable units; when a user asks a question, relevant chunks are retrieved and fed to the LLM as context. (2) LLM context windows — models have token limits (8K, 32K, 128K); chunk longer documents to process them in batches. (3) Document processing pipelines — split large PDFs or web-scraped content for parallel processing. (4) Translation — chunk long texts before sending to translation APIs that have character limits. Best practices: choose chunk size based on your embedding model's optimal input length (e.g., 512 tokens for text-embedding-ada-002), always use overlap (10–20%) to avoid splitting key concepts across chunks, prefer sentence/paragraph boundaries over raw character limits to keep semantic units intact, and preserve metadata (source document, position, page number) with each chunk for traceability.