Evaluation Frameworks
Several frameworks automate RAG evaluation, generating scores for faithfulness, relevance, and more. The most popular are Ragas, TruLens, and DeepEval.
Ragas (RAG Assessment)
Open‑source, specifically designed for RAG. Computes metrics using an LLM (e.g., GPT‑3.5).
from datasets import Dataset
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_recall
dataset = Dataset.from_dict({
"question": ["What is RAG?"],
"answer": ["RAG is retrieval-augmented generation..."],
"contexts": [["RAG combines retrieval and generation..."]],
"ground_truth": ["RAG stands for retrieval-augmented generation"]
})
result = evaluate(dataset, metrics=[faithfulness, answer_relevancy, context_recall])TruLens
Provides a dashboard to track experiments, compare models, and debug RAG pipelines. Includes feedback functions for relevance, groundedness, and QA correctness.
from trulens_eval import TruChain
tru = TruChain(chain, app_id='rag_app')
tru.run(question="What is RAG?")
tru.get_leaderboard()DeepEval
Lightweight, uses OpenAI or local models for evaluation. Supports faithfulness, answer relevancy, context relevancy, and more.
Two Minute Drill
- Ragas: open‑source, metrics for retrieval and generation.
- TruLens: dashboard, experiment tracking.
- DeepEval: lightweight, easy integration.
- All use an LLM to judge quality.
Need more clarification?
Drop us an email at career@quipoinfotech.com