Typical Problem Scenarios
Concepts are often echoed back and forth in technical documentation, and traditional chunking strategies can fragment the integrity of technical points. dsRAG's innovative approach includes:
optimization strategy
- Dynamic Window Extensions: By
context_window=1024Parameters control the scope of contextual associations - Hierarchical Index Construction: Create a tree index of chapters-subchapters (to be used in conjunction with the
hierarchical=True(Parameters) - Terminology consistency maintenance: Use
term_consistency_checkerEnsure uniform interpretation of acronyms
Implementation process
- Pre-segmented documents:
create_kb_from_file('manual', 'user_guide.pdf', pre_segment=True) - Set up a glossary of technical terms:
kb.add_glossary('AI', 'Artificial Intelligence') - Explicit association at query time:
query('How to calibrate?', link_sections=['Troubleshooting','Appendix B'])
performance trade-off
It is recommended to balance the quality and speed of retrieval:
- Used in the development phaseexhaustive_search=True
- The production environment is switched toapproximate_searchparadigm
This answer comes from the articledsRAG: A Retrieval Engine for Unstructured Data and Complex QueriesThe
May not be reproduced without permission:AI productivity tools " How to optimize a RAG system to handle long contextual dependencies on 100+ pages of technical documentation?
