Definition
Chunking is a critical preprocessing step in Retrieval-Augmented Generation (RAG) and other NLP pipelines. It involves dividing text into fixed-size or semantic units (chunks) to fit within the context window limits of language models. Effective chunking strategies balance context preservation with retrieval accuracy, ensuring that each segment contains sufficient information to be useful when queried. This technique enables the handling of vast amounts of data that exceed the memory constraints of individual model inputs.
Summary
Chunking is the process of splitting large documents or data streams into smaller, manageable segments for processing or indexing.
Key Concepts
- Context Window
- RAG
- Text Splitting
- Indexing
Use Cases
- Building knowledge bases for RAG
- Processing long documents for summarization
- Vector database ingestion