Large Document Optimization Strategy
Implement a hierarchical processing scheme for the three major performance bottlenecks of large-volume PDFs:
- segmentation optimization::
- Set max_section_length=200 in preprocess.py
- Enable smart_chunking algorithm to maintain paragraph integrity
- Automatic identification of chapter structure for technical documents
- Resource management::
- Configuring the GPU memory hierarchy loading mechanism
- Reduce memory footprint with memmap technology
- Enable background_indexing background indexing
Performance data::
- Processing time reduced from 42 minutes (traditional program) to 8 minutes
- Reduced video memory footprint by 67%
- Supports up to 2000 pages of single document processing
suggestion: The scanned version of the PDF is recommended to use external OCR tools to pre-process first, which can then improve the processing speed of 30%.
This answer comes from the articleLocalPdfChatRAG: Intelligent Chat Tool to Support Local Multi-Source PDF Document Q&AThe
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