The tool's built-in multi-stage preprocessing pipeline enables intelligent optimization of input data. Its core components include: a stop word filter, a punctuation normalization module, a case converter, and a tiktoken-based token compression algorithm.
In the GitHub repository processing scenario, generated files such as *.pb.go can be automatically ignored with the excluded_patterns parameter; the EXCLUDED_DIRS setting can exclude non-core directories such as tests. Practical tests show that these preprocesses reduce the input size of code analysis scenarios by 58% on average.
The specially designed dual output mode (compressed/uncompressed) preserves the original information while providing an optimized version. User cases show that when processing a 300-page PDF paper, the compressed output reduces the number of tokens from 120,000 to 47,000, a perfect fit for the context window limitations of most LLMs.
This answer comes from the articleOneFileLLM: Integrating Multiple Data Sources into a Single Text FileThe
