The following optimizations are recommended for financial/medical and other private data scenarios:
- Localized Deployment: Install via git clone instead of API call to avoid sensitive data outflow.
- Modular customization: Turn off non-essential modules (e.g., remove -use_routing parameter) to reduce data exposure
- Intranet Data Source Configuration: Point database paths to internal servers to ensure no connection to extranet knowledge base
- Log Control: Periodically clean up intermediate result files in the outputs/ directory
- Performance Monitoring: Analyze the time-consumption metrics in overall_results.txt to optimize the efficiency of SQL queries or JSON parsing.
Implementation Example: When analyzing a patient database using Graph schema, a hospital increased query speed by 40% while ensuring HIPAA compliance by disabling the reflection mechanism (remove-use_reflection) and setting up a data cache.
This answer comes from the articleDeepSieve: a RAG Intelligent Information Screening Tool for Processing Complex Query SourcesThe
May not be reproduced without permission:AI productivity tools " How to optimize DeepSieve's performance in private data scenarios?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil