LangExtract handles long documents through the following mechanism:
- Intelligent chunking: automatically splits long documents into appropriately sized text blocks
- Parallel processing: by setting the
max_workersParameter to control the number of threads (e.g., 4 threads if processing the entire Romeo and Juliet book) - Multi-round extraction: by
num_passesParameter settings are extracted multiple times to improve accuracy
Optimization Recommendations:
- Tier 2 Gemini quotas are recommended to avoid rate limiting when processing very long documents
- For complex documents it is possible to switch to a more powerful model (e.g. from the
gemini-2.5-flashSwitch togemini-2.5-pro) - Ensure stable network connections, especially when using cloud-based models
- The results can be saved using the
save_annotated_documentsmethod generates a JSONL format file
This answer comes from the articleLangExtract: open source tools to extract structured data from textThe
May not be reproduced without permission:AI productivity tools " What is LangExtract's mechanism for handling long documents? What are some optimization suggestions?
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