Principle:Intel Ipex llm Document Chunking
| Knowledge Sources | |
|---|---|
| Domains | NLP, RAG, Data_Processing |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Technique for splitting source documents into smaller, overlapping chunks suitable for embedding and retrieval in RAG pipelines.
Description
Document Chunking divides large text documents into smaller pieces (chunks) that fit within the context window of embedding models and can be independently embedded into a vector store. The CharacterTextSplitter splits text by character count with configurable chunk size and overlap. Overlap ensures that context spanning chunk boundaries is preserved. Chunk size must balance between preserving context (larger chunks) and precision of retrieval (smaller chunks).
Usage
Use this as the first step in any RAG pipeline where source documents need to be indexed. Apply before embedding generation and vector store insertion.
Theoretical Basis
# Abstract chunking logic (NOT real implementation)
# Given text T of length N, chunk_size=1000, overlap=0:
# chunks = [T[0:1000], T[1000:2000], T[2000:3000], ...]
# With overlap=200:
# chunks = [T[0:1000], T[800:1800], T[1600:2600], ...]
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Implementation
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