ragas

name	ragas
description	Evaluation framework for RAG pipelines measuring faithfulness, relevance, and answer correctness
version	1.0.0
tags	["llm","rag","evaluation","metrics","testing","quality-assurance"]

Ragas — RAG Evaluation Framework

Overview

Ragas (RAG Assessment) is a Python framework for evaluating Retrieval-Augmented Generation pipelines without requiring human-annotated ground truth labels. It computes reference-free metrics (faithfulness, answer relevance, context precision/recall) and reference-based metrics (answer correctness) using LLMs as judges. Integrates with LangChain, LlamaIndex, and Haystack.

GitHub: https://github.com/explodinggradients/ragas (8k+ stars)

When to Use

• Evaluating RAG pipeline quality before deployment
• A/B testing different retrieval strategies or chunk sizes
• Monitoring RAG quality in production over time
• Automated regression testing for RAG systems
• Debugging poor RAG responses with metric-level analysis

Installation

pip install ragas

# With specific integrations
pip install ragas langchain

Key Patterns / Usage

Core Metrics Overview

- faithfulness: Are answers faithful to the retrieved context? (0-1)
- answer_relevancy: Does the answer address the question? (0-1)
- context_precision: Is retrieved context relevant and precise? (0-1)
- context_recall: Does context cover all info needed? (needs ground truth, 0-1)
- answer_correctness: Is the answer factually correct? (needs ground truth, 0-1)

Basic Evaluation Dataset

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision
from datasets import Dataset

# Prepare evaluation data
eval_data = {
    "question": [
        "What is the capital of France?",
        "Who wrote Harry Potter?",
        "When was Python created?",
    ],
    "answer": [
        "The capital of France is Paris.",
        "Harry Potter was written by J.K. Rowling.",
        "Python was created in 1991 by Guido van Rossum.",
    ],
    "contexts": [
        ["Paris is the capital and largest city of France."],
        ["J.K. Rowling wrote the Harry Potter series starting in 1997."],
        ["Python is a programming language created by Guido van Rossum in 1991."],
    ],
    # Optional: ground truth for reference-based metrics
    "ground_truth": [
        "Paris is the capital of France.",
        "J.K. Rowling wrote Harry Potter.",
        "Python was created in 1991.",
    ],
}

dataset = Dataset.from_dict(eval_data)

# Run evaluation
result = evaluate(
    dataset=dataset,
    metrics=[faithfulness, answer_relevancy, context_precision],
)

print(result)
# {'faithfulness': 0.97, 'answer_relevancy': 0.95, 'context_precision': 0.92}

Evaluate a LangChain RAG Pipeline

from ragas.integrations.langchain import EvaluatorChain
from ragas.metrics import faithfulness, answer_relevancy
from langchain.chains import RetrievalQA

# Your LangChain RAG chain
qa_chain = RetrievalQA.from_chain_type(...)

# Wrap with Ragas evaluator
evaluator = EvaluatorChain(metric=faithfulness)

# Run and evaluate
questions = ["What is machine learning?", "How does RAG work?"]
for question in questions:
    result = qa_chain({"query": question})
    score = evaluator({"question": question, "answer": result["result"], "contexts": result["source_documents"]})
    print(f"Q: {question} | Faithfulness: {score['faithfulness']:.2f}")

With Custom LLM Judge

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy
from ragas.llms import LangchainLLMWrapper
from langchain_anthropic import ChatAnthropic

# Use Claude as the evaluation LLM
claude = ChatAnthropic(model="claude-3-5-haiku-20241022")
ragas_llm = LangchainLLMWrapper(claude)

result = evaluate(
    dataset=dataset,
    metrics=[faithfulness, answer_relevancy],
    llm=ragas_llm,
)
print(result)

Testset Generation (No Ground Truth Needed)

from ragas.testset import TestsetGenerator
from ragas.testset.evolutions import simple, reasoning, multi_context
from langchain_community.document_loaders import DirectoryLoader
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

# Load your documents
loader = DirectoryLoader("./docs/", glob="**/*.txt")
documents = loader.load()

# Generate test questions from your documents
generator = TestsetGenerator.from_langchain(
    generator_llm=ChatOpenAI(model="gpt-4o"),
    critic_llm=ChatOpenAI(model="gpt-4o"),
    embeddings=OpenAIEmbeddings(),
)

testset = generator.generate_with_langchain_docs(
    documents,
    test_size=20,
    distributions={simple: 0.5, reasoning: 0.25, multi_context: 0.25},
)

# Convert to dataset for evaluation
eval_df = testset.to_pandas()
print(eval_df.head())

Scoring Individual Samples

from ragas.metrics import Faithfulness
from ragas.dataset_schema import SingleTurnSample
import asyncio

metric = Faithfulness()

sample = SingleTurnSample(
    user_input="What is the speed of light?",
    response="The speed of light is approximately 3×10^8 m/s in a vacuum.",
    retrieved_contexts=["The speed of light in a vacuum is 299,792,458 meters per second."],
)

score = asyncio.run(metric.single_turn_ascore(sample))
print(f"Faithfulness score: {score:.3f}")

Batch Evaluation with Metrics Comparison

import pandas as pd
from ragas import evaluate
from ragas.metrics import (
    faithfulness,
    answer_relevancy,
    context_precision,
    answer_correctness,
)

# Evaluate two RAG configurations
results_v1 = evaluate(dataset_v1, metrics=[faithfulness, answer_relevancy, context_precision])
results_v2 = evaluate(dataset_v2, metrics=[faithfulness, answer_relevancy, context_precision])

comparison = pd.DataFrame({
    "Metric": ["Faithfulness", "Answer Relevancy", "Context Precision"],
    "Config V1": [results_v1["faithfulness"], results_v1["answer_relevancy"], results_v1["context_precision"]],
    "Config V2": [results_v2["faithfulness"], results_v2["answer_relevancy"], results_v2["context_precision"]],
})
print(comparison)

Production Monitoring

import pandas as pd
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy

def evaluate_production_batch(logs: list[dict]) -> pd.DataFrame:
    """Evaluate a batch of production RAG logs."""
    dataset = Dataset.from_dict({
        "question": [log["query"] for log in logs],
        "answer": [log["response"] for log in logs],
        "contexts": [log["retrieved_docs"] for log in logs],
    })
    
    result = evaluate(dataset, metrics=[faithfulness, answer_relevancy])
    return result.to_pandas()

# Log failing responses for investigation
df = evaluate_production_batch(recent_logs)
failing = df[df["faithfulness"] < 0.5]
print(f"Low faithfulness cases: {len(failing)}")

Common Pitfalls

• API costs: Ragas uses LLMs as judges — evaluating 100 samples costs real API money; budget accordingly
• No ground truth needed for faithfulness/relevancy; required for context_recall/answer_correctness
• Context format: pass contexts as a list of strings per question, not a single string
• OpenAI default: Ragas defaults to OpenAI; explicitly configure if using Anthropic or local models
• Score interpretation: scores are relative, not absolute; track trends over time rather than fixating on a threshold
• Async: modern Ragas uses async scoring; wrap in asyncio.run() if not in async context

Related Skills

• rag-pipeline — building RAG systems to evaluate
• llm-evaluation — general LLM evaluation patterns
• haystack — pipeline framework with built-in evaluation
• mteb — embedding model benchmarks
• data-quality-validation — general data quality patterns

GitNexus Index

tool: ragas
category: llm-evaluation
tier: library
interface: python-sdk
platform: cross-platform
stars: 8000+