Filtering techniques to address RAG context redundancy
Ragas'context_relevancyMetrics are specifically used to measure signal-to-noise ratios:
- Problem diagnosis:The degree of redundancy was identified by calculating the percentage of essential sentences, with a score of 0 indicating completely irrelevant and 1 indicating completely relevant
- Solution:
- Retrieval phase:Add similarity threshold filtering (recommended to keep paragraphs with cosine > 0.7)
- Preprocessing:Applying text summarization techniques to compress context
- Post-processing:Using Ragas to Identify Key Sentences as Generation Constraints
- Implementation tools:
- Configure ragas.metrics.context_relevancy evaluation
- Analyzing sentence necessity in conjunction with agr_score
- Optimize search results based on overlap_scores
The method is described in detail in the technical implementation section of the article and can effectively improve the context utilization of 20% or more.
This answer comes from the articleRagas: assessing RAG recall QA accuracy and answer correlationThe
May not be reproduced without permission:AI productivity tools " How to overcome the degradation of effectiveness due to redundancy of contextual information in RAG systems?
