Principle:Confident ai Deepeval Document Chunking
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| Last Updated | 2026-02-14 09:00 GMT |
Overview
Document chunking is the process of splitting large documents into smaller, manageable segments for use in synthetic evaluation data generation. Proper chunking ensures that generated test data is grounded in coherent, self-contained passages rather than arbitrary text fragments.
Description
When generating synthetic evaluation datasets from source documents, the quality of the generated data depends heavily on how the source material is segmented. Document chunking addresses this by:
- Breaking documents into token-bounded segments -- ensuring each chunk fits within LLM context windows and produces focused evaluation questions.
- Preserving semantic coherence -- chunks should represent complete ideas or sections, not arbitrary splits mid-sentence or mid-paragraph.
- Controlling granularity -- chunk size determines the specificity of generated questions; smaller chunks yield more focused questions while larger chunks enable broader, multi-fact questions.
- Supporting overlap strategies -- overlapping chunks ensure that information at chunk boundaries is not lost, improving coverage of source material.
In the DeepEval framework, document chunking is the first stage of the synthetic data generation pipeline. Documents are loaded from various file formats (PDF, TXT, DOCX, MD), then split into chunks that serve as contexts for downstream question and answer generation.
Usage
Document chunking is applied whenever evaluation data must be generated from raw source documents. The chunking strategy (chunk size, overlap, and embedding model) directly affects:
- The number and diversity of generated evaluation goldens
- The factual density of each generated question-answer pair
- The quality of context retrieval during golden generation
Theoretical Basis
Document chunking for synthetic data generation draws from several established techniques:
- Text segmentation -- dividing text at natural boundaries (sentences, paragraphs, sections) to maintain readability and semantic integrity.
- Token-based chunking -- splitting text based on token counts rather than character counts, aligning with LLM tokenization to ensure chunks fit within model context limits.
- Overlap strategies -- including a configurable number of overlapping tokens between adjacent chunks to prevent information loss at boundaries. This is analogous to sliding-window approaches in NLP.
- Embedding-aware chunking -- using embedding models to inform chunk boundaries, ensuring that semantically related content remains within the same chunk.
The abstract chunking process follows this pattern:
DOCUMENT_CHUNKING(document, chunk_size, chunk_overlap):
1. LOAD document from file (PDF, TXT, DOCX, MD)
2. TOKENIZE document content using encoding model
3. SPLIT into segments of chunk_size tokens
4. APPLY overlap of chunk_overlap tokens between adjacent segments
5. RETURN list of text chunks
Key properties:
- Completeness -- the union of all chunks covers the entire source document.
- Bounded size -- each chunk is guaranteed to be at most chunk_size tokens.
- Overlap continuity -- adjacent chunks share chunk_overlap tokens, ensuring boundary information is preserved.