Chunk granularity
Chunk granularity is the amount of data or content assigned to each unit when a larger source is divided into chunks for processing, storage, transfer, or retrieval.
Fast Python: High performance techniques for large datasets
- How it is used
- Configure how large arrays are partitioned for processing and storage.
- How deep it goes
- Explains chunk-shape choices and storage trade-offs, but leaves workload-specific calibration to the user.
- Examples
- Compares 41,632-element chunks with 20,000-by-210 chunks for arrays of different shapes.
- What you can do after
- Configure chunk dimensions and calibrate their size against access patterns, memory, and storage limits.
Read how Fast Python: High performance techniques for large datasets explains chunk granularity →
AI Agents and Applications
Chunk size is the amount of text contained in each document chunk produced for separate processing or retrieval.
- How it is used
- Matches chunk size to expected question breadth and document structure to improve retrieval relevance and answer context.
- How deep it goes
- Explains retrieval trade-offs, splitting strategies, metadata considerations, and parent-child implementation, while deferring exhaustive size optimization to testing.
- Examples
- Uses hierarchical and fixed-size splitting, then demonstrates 3,000-character parents with 500-character children in ParentDocumentRetriever.
- What you can do after
- Apply it via document splitters and ParentDocumentRetriever, then test sizes against query breadth, context needs, and retrieval precision.
Read how AI Agents and Applications explains chunk granularity →
Building Reliable AI Systems
The size of a text segment produced when source content is divided for processing or retrieval.
- How it is used
- Tunes text segmentation in retrieval-augmented generation to balance useful context against retrieval noise or missing details.
- How deep it goes
- Explains chunk-size trade-offs and configuration testing, but does not detail general chunking algorithms.
- Examples
- Compares five character-based sizes and gives a policy-document example using 500-character chunks with 100-character overlap.
- What you can do after
- Evaluate chunk-size configurations by comparing retrieval-augmented generation experiments on test data.
Read how Building Reliable AI Systems explains chunk granularity →
Related concepts
- pandasconfigures · pandas is an open-source Python library that provides labeled data structures and tools for manipulating and analyzing tabular and other structured data.
- bufferingconfigures · Buffering is the temporary storage of data or work between production and consumption to accommodate differences in processing rates, absorb interruptions, or improve transfer efficiency.
- text chunkconfigures · A text chunk is a bounded segment of a larger text treated as a unit for processing, storage, or retrieval.
- information retrievalconfigures · Information retrieval is the process of locating and selecting relevant information from an available collection in response to a query or information need.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.