The technical architecture of XRAG, a professional RAG assessment framework, contains the following core functional modules:
- assessment system: Integration of traditional metrics (F1 values, exact match rates) with new LLM-based metrics (factual consistency, contextual relevance)
- Search SupportSupport BM25 probabilistic search, vector semantic search, tree structure search and other diversified search methods
- model compatibility: supports both cloud APIs such as OpenAI and local models such as Qwen and LLaMA.
- interactive interface: Provides both command-line tools and visual Web UI.
Notable technical features include:
- Modular design allows components to be tested independently of each other.
- Quick validation of benchmark datasets such as pre-built HotpotQA
- Supports extended development of customized search strategies
- Evaluation results visualize performance bottlenecks through visual charts
This design allows the XRAG to meet the rigor requirements of academic research while adapting to the rapid iteration needs of industry.
This answer comes from the articleXRAG: A Visual Evaluation Tool for Optimizing Retrieval Enhancement Generation SystemsThe
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