DeepSieve ensures retrieval reliability through a three-layer mechanism:
- Preventive mechanisms: Ensure that all dependent libraries (e.g. scikit-learn) are correctly versioned when installing, and configure a valid LLM API key
- Runtime security: The enable reflection parameter (-use_reflection -max_reflexion_times 2) allows automatic retry of failed retrievals
- after-the-fact diagnosis: Check the logs for failure entries, common problems include:
1. Wrong path to data source - check database address in configuration file
2. API flow limiting - adjusting query frequency or upgrading keys
3. Querying ambiguity - optimizing the formulation of the original problem - Performance Tuning: if it fails repeatedly, try switching RAG types (naive/graph) or simplifying query complexity
Practical tips: When applied in the biomedical field, it is recommended to add synonym extensions for standard terms, which can reduce search failures due to terminological differences.
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 can I completely solve the problem of incomplete answers due to failed searches in the RAG system?
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