How to optimize retrieval speed for large-scale document management?

2025-08-27

1.4 K

Three-tier acceleration solution for millisecond response

For the retrieval delay problem that occurs when the document volume exceeds 500,000, a combined strategy is recommended:

Hierarchical caching mechanism: Usecache_documents(filters={})Preloading high-frequency access data (e.g., the last 3 months of documents) has been measured to reduce the real-time computational load by 80%.
Storage Optimization: Byconnect_storage()Access to PostgreSQL/MongoDB and other professional databases, 5-8 times higher throughput than the default SQLite, need to pay attention to the configuration of the index:
```
CREATE INDEX idx_category ON documents USING GIN(metadata);
```
Batch Processing Techniques(1) Useingest_directory()Setting during batch importbatch_size=500Balance memory footprint 2) Non-immediate demand documents set tobackground_processing=TrueThe

Enterprise user cases show that the solution has reduced the average retrieval time from 12s to 1.3s for a 1.2TB research literature library.