DocAILab/XRAG
XRAG: eXamining the Core - Benchmarking Foundational Component Modules in Advanced Retrieval-Augmented Generation
Provides modular benchmarking for RAG systems through pluggable retrievers (vector, BM25, hybrid, tree-based), embeddings, and LLMs with comprehensive evaluation metrics spanning traditional (F1, NDCG), LLM-based (faithfulness, correctness), and deep evaluation dimensions. Implements agentic RAG workflows via five orchestrator types (sequential, conditional, iterative, parallel, hybrid) and integrates with OpenAI APIs, local models (Qwen, LLaMA via Ollama), and vector databases for end-to-end evaluation pipelines.
120 stars.
Stars
120
Forks
18
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 07, 2026
Commits (30d)
0
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