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Documentation Index

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RAGOpt provides metrics to evaluate retrieval quality, generation performance, and operational costs in your RAG pipeline.

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Metric Categories

Retrieval Metrics

Document retrieval quality, relevance, and ranking

Generation Metrics

Response quality, safety, and alignment

Full Pipeline Metrics

Cost and latency across the entire pipeline

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Quick Start

from rag_opt.eval.eval import RAGEvaluator
from rag_opt import init_chat_model
from rag_opt.dataset import TrainDataset

# Setup RAG
llm = init_chat_model(model="gpt-3.5-turbo", model_provider="openai", api_key=OPENAI_API_KEY)
dataset = TrainDataset.from_json("./rag_dataset.json")
embeddings = init_embeddings(model="all-MiniLM-L6-v2", model_provider="huggingface",api_key=HUGGINFACE_API_KEY)
vector_store = init_vectorstore(provider="faiss",embeddings=embeddings,)
rag = RAGWorkflow(
    embeddings=embeddings,
    vector_store=vector_store,
    llm=llm,
    retrieval_config={
        "search_type": "hybrid",
        "k": 3
    },
)

# generate evaluation dataset (to be used by evaluator)
train_dataset = TrainDataset.from_json("./rag_dataset.json")
eval_dataset = rag.get_batch_answers(train_dataset)


# Setup evaluator
evaluator = RAGEvaluator(
    evaluator_llm=llm,
    evaluator_embedding=embeddings
)
results = evaluator.evaluate(eval_dataset, normalize=True,return_tensor=False))

# Print results
for metric_name, result in results.items():
    print(f"{metric_name}: {result.value}")

print(f"\nOverall Score: {evaluator.compute_objective_score(results)}")

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How It Works

Metrics return a MetricResult with:
  • name: Metric identifier
  • value: Aggregated score (0-1 scale)
  • category: RETRIEVAL, GENERATION, or FULL
  • metadata: Individual scores and details
  • error: Error message if failed

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LLM-Based vs Non-LLM Metrics

LLM-Based (SafetyMetric, AlignmentMetric, ContextPrecision)
  • Use an LLM judge for quality assessment
  • Require llm parameter at initialization
  • Batch process for efficiency
Non-LLM (CostMetric, LatencyMetric, MRR, NDCG)
  • Direct calculation without LLM calls
  • Faster and deterministic

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Custom Metrics

class CustomMetric(BaseMetric):
    is_llm_based: bool = False # Make True if this metric requires llm
    category: MetricCategory = MetricCategory.GENERATION
    name: str = "my_custom_metric"

    def __init__(self):
        super().__init__(
            negate=False,  # True if lower is better
            worst_value=0.0
        )
    def _evaluate(self, dataset:EvaluationDataset, **kwargs) -> list[float]:
        """ Your evaluation logic here"""
        return [0.85 for _ in dataset.items]

evaluator.add_metric(CustomMetric(), weight=0.5)

​
Advanced Usage

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Selective Evaluation

from rag_opt.eval import RAGEvaluator,ContextPrecision, MRR, NDCG,SafetyMetric, AlignmentMetric
from rag_opt import init_chat_model,init_embeddings


llm = init_chat_model(model="gpt-3.5-turbo", model_provider="openai", api_key=OPENAI_API_KEY)
embeddings = init_embeddings(model="all-MiniLM-L6-v2", model_provider="huggingface",api_key=HUGGINFACE_API_KEY)

# Retrieval only
retrieval_evaluator = RAGEvaluator(
    metrics=[ContextPrecision(llm=llm), MRR(embedding_model=embeddings), NDCG(embedding_model=embeddings)]
)

# Generation only
generation_evaluator = RAGEvaluator(
    metrics=[SafetyMetric(llm), AlignmentMetric(llm)]
)

results = retrieval_evaluator.evaluate(eval_dataset, normalize=True,return_tensor=False)
results2 = generation_evaluator.evaluate(eval_dataset, normalize=True,return_tensor=False)

# Print results
for metric_name, result in results.items():
    print(f"{metric_name}: {result.value}")

for metric_name, result in results2.items():
    print(f"{metric_name}: {result.value}")

print(f"\nOverall Score: {evaluator.compute_objective_score(results)}")