Principle:Ucbepic Docetl Document Length Assessment
| Knowledge Sources | |
|---|---|
| Domains | NLP, Token_Management |
| Last Updated | 2026-02-08 01:40 GMT |
Overview
A measurement principle that estimates document token counts to determine whether chunking is necessary and to calculate optimal chunk sizes.
Description
Document Length Assessment measures the token length of documents relative to LLM context windows. When documents exceed the model's context limit, they must be split into chunks. This principle involves:
- Counting tokens using model-specific tokenizers (e.g., tiktoken)
- Comparing document lengths against context window limits
- Generating recommended chunk sizes based on the document length distribution
- Determining whether the split-gather-map-reduce pattern is needed
Usage
Apply this principle at the start of any pipeline processing long documents (legal filings, transcripts, research papers). If documents exceed the target model's context window, chunking operations must be added to the pipeline.
Theoretical Basis
Token counting and chunk size estimation:
- Tokenization: Convert text to tokens using the target model's tokenizer
- Distribution Analysis: Measure token count distribution across the dataset
- Budget Allocation: Reserve tokens for prompt, instructions, and output within the context window
- Chunk Sizing: Calculate chunk sizes that fit within the remaining token budget
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