Paste long text for chunking, optimized for RAG retrieval-augmented generation systems.
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.
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.
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.
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.
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.
No. All text chunking is processed locally in your browser. No data is sent to any server.