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RAGAS

The RAGAS metric is the average of four distinct metrics:

  • RAGASAnswerRelevancyMetric
  • RAGASFaithfulnessMetric
  • RAGASContextualPrecisionMetric
  • RAGASContextualRecallMetric

It provides a score to holistically evaluate of your RAG pipeline's generator and retriever.

WHAT'S THE DIFFERENCE?

The RAGASMetric uses the ragas library under the hood and are available on deepeval with the intention to allow users of deepeval can have access to ragas in deepeval's ecosystem as well. They are implemented in an almost identical way to deepeval's default RAG metrics. However there are a few differences, including but not limited to:

  • deepeval's RAG metrics generates a reason that corresponds to the score equation. Although both ragas and deepeval has equations attached to their default metrics, deepeval incorperates an LLM judges' reasoning along the way.
  • deepeval's RAG metrics are debuggable - meaning you can inspect the LLM judges' judgements along the way to see why the score is a certain way.
  • deepeval's RAG metrics are JSON confineable. You'll often meet NaN scores in ragas because of invalid JSONs generated - but deepeval offers a way for you to use literally any custom LLM for evaluation and JSON confine them in a few lines of code.
  • deepeval's RAG metrics integrates fully with deepeval's ecosystem. This means you'll get access to metrics caching, native support for pytest integrations, first-class error handling, available on Confident AI, and so much more.

Due to these reasons, we highly recommend that you use deepeval's RAG metrics instead. They're proven to work, and if not better according to examples shown in some studies.

Required Arguments​

To use the RagasMetric, you'll have to provide the following arguments when creating an LLMTestCase:

  • input
  • actual_output
  • expected_output
  • retrieval_context

Example​

First, install ragas:

pip install ragas

Then, use it within deepeval:

from deepeval import evaluate
from deepeval.metrics.ragas import RagasMetric
from deepeval.test_case import LLMTestCase

# Replace this with the actual output from your LLM application
actual_output = "We offer a 30-day full refund at no extra cost."

# Replace this with the expected output from your RAG generator
expected_output = "You are eligible for a 30 day full refund at no extra cost."

# Replace this with the actual retrieved context from your RAG pipeline
retrieval_context = ["All customers are eligible for a 30 day full refund at no extra cost."]

metric = RagasMetric(threshold=0.5, model="gpt-3.5-turbo")
test_case = LLMTestCase(
    input="What if these shoes don't fit?",
    actual_output=actual_output,
    expected_output=expected_output,
    retrieval_context=retrieval_context
)

metric.measure(test_case)
print(metric.score)

# or evaluate test cases in bulk
evaluate([test_case], [metric])

There are three optional parameters when creating a RagasMetric:

  • [Optional] threshold: a float representing the minimum passing threshold, defaulted to 0.5.
  • [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any one of langchain's chat models of type BaseChatModel. Defaulted to 'gpt-3.5-turbo'.
  • [Optional] embeddings: any one of langchain's embedding models of type Embeddings. Custom embeddings provided to the RagasMetric will only be used in the RAGASAnswerRelevancyMetric, since it is the only metric that requires embeddings for calculating cosine similarity.
info

You can also choose to import and execute each metric individually:

from deepeval.metrics.ragas import RAGASAnswerRelevancyMetric
from deepeval.metrics.ragas import RAGASFaithfulnessMetric
from deepeval.metrics.ragas import RAGASContextualRecallMetric
from deepeval.metrics.ragas import RAGASContextualPrecisionMetric

These metrics accept the same arguments as the RagasMetric.