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Benchmark enterprise RAG agents with EnterpriseRAG-Bench
Use EnterpriseRAG-Bench to evaluate an enterprise RAG or knowledge-agent system against a realistic synthetic company corpus with answer, recall, and comparative scoring.
skills/benchmark-enterprise-rag-agents-with-enterpriserag-bench/SKILL.md
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# Benchmark enterprise RAG agents with EnterpriseRAG-Bench
Use EnterpriseRAG-Bench to evaluate an enterprise RAG or knowledge-agent system against a realistic synthetic company corpus with answer, recall, and comparative scoring.
## Prerequisites
Python 3.10+, EnterpriseRAG-Bench dataset and questions, a RAG or knowledge-agent system under test, OpenAI or Anthropic compatible LLM credentials for evaluation
## Installation
Install or set up from the source-backed instructions:
Clone https://github.com/onyx-dot-app/EnterpriseRAG-Bench, install Python dependencies with pip install -r requirements.txt, set LLM_PROVIDER and LLM_API_KEY, download the benchmark dataset from the latest GitHub release or Hugging Face, write system outputs to answer_evaluation/answers.jsonl, then run python -m src.scripts.answer_evaluation.metrics_based_eval --answers-file answer_evaluation/answers.jsonl.
