Modular Optimization Solution for XRAG
To improve the retrieval accuracy of the RAG system, XRAG provides a three-level optimization path:
- Search strategy selection:Supports BM25 (keyword matching), vector search (semantic matching) and hybrid search with different scenario suggestions:
- Terminology Search Priority BM25
- Open field problem searching with vectors
- Complex Problems Retrieved Using Tree Structures
- Query refactoring module:Optimizing raw query statements through LLM enables XRAG's built-in Query Rewriting feature to be configured:
- Modify rewrite_module=true in config.toml
- Choosing OpenAI or local Qwen as a rewriting model
- Evaluate feedback loops:Failure cases are analyzed using 50+ assessment metrics (especially MRR and NDCG) and XRAG visualization reports are labeled:
- Problems with ranking of search results
- Types of query with insufficient recall
- Vector space matching bias
In practice, you can use the Web UI to quickly test different configurations, and then batch verify the optimal solution via the command line.
This answer comes from the articleXRAG: A Visual Evaluation Tool for Optimizing Retrieval Enhancement Generation SystemsThe
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