Traditional NLP Metrics
Non LLM String Similarity
NonLLMStringSimilarity metric measures the similarity between the reference and the response using traditional string distance measures such as Levenshtein, Hamming, and Jaro. This metric is useful for evaluating the similarity of response to the reference text without relying on large language models (LLMs). The metric returns a score between 0 and 1, where 1 indicates a perfect match between the response and the reference. This is a non LLM based metric.
Example
from ragas.dataset_schema import SingleTurnSample
from ragas.metrics._string import NonLLMStringSimilarity
sample = SingleTurnSample(
response="The Eiffel Tower is located in India.",
reference="The Eiffel Tower is located in Paris."
)
scorer = NonLLMStringSimilarity()
await scorer.single_turn_ascore(sample)
One can choose from available string distance measures from DistanceMeasure. Here is an example of using Hamming distance.
from ragas.metrics._string import NonLLMStringSimilarity, DistanceMeasure
scorer = NonLLMStringSimilarity(distance_measure=DistanceMeasure.HAMMING)
BLEU Score
The BleuScore score is a metric used to evaluate the quality of response by comparing it with reference. It measures the similarity between the response and the reference based on n-gram precision and brevity penalty. BLEU score was originally designed to evaluate machine translation systems, but it is also used in other natural language processing tasks. Since it was designed to evaluate machine translation systems, it expects the response and reference to contain same number of sentences. The comparison is done at sentence level. BLEU score ranges from 0 to 1, where 1 indicates a perfect match between the response and the reference. This is a non LLM based metric.
Example
from ragas.dataset_schema import SingleTurnSample
from ragas.metrics import BleuScore
sample = SingleTurnSample(
response="The Eiffel Tower is located in India.",
reference="The Eiffel Tower is located in Paris."
)
scorer = BleuScore()
await scorer.single_turn_ascore(sample)
ROUGE Score
The RougeScore score is a set of metrics used to evaluate the quality of natural language generations. It measures the overlap between the generated response and the reference text based on n-gram recall, precision, and F1 score. ROUGE score ranges from 0 to 1, where 1 indicates a perfect match between the response and the reference. This is a non LLM based metric.
from ragas.dataset_schema import SingleTurnSample
from ragas.metrics import RougeScore
sample = SingleTurnSample(
response="The Eiffel Tower is located in India.",
reference="The Eiffel Tower is located in Paris."
)
scorer = RougeScore()
await scorer.single_turn_ascore(sample)
You can change the rouge_type to rouge-1, rouge-2, or rouge-l to calculate the ROUGE score based on unigrams, bigrams, or longest common subsequence respectively.
You can change the measure_type to precision, recall, or f1 to calculate the ROUGE score based on precision, recall, or F1 score respectively.
Exact Match
The ExactMatch metric checks if the response is exactly the same as the reference text. It is useful in scenarios where you need to ensure that the generated response matches the expected output word-for-word. For example, arguments in tool calls, etc. The metric returns 1 if the response is an exact match with the reference, and 0 otherwise.
from ragas.dataset_schema import SingleTurnSample
from ragas.metrics import ExactMatch
sample = SingleTurnSample(
response="India",
reference="Paris"
)
scorer = ExactMatch()
await scorer.single_turn_ascore(sample)
String Presence
The StringPresence metric checks if the response contains the reference text. It is useful in scenarios where you need to ensure that the generated response contains certain keywords or phrases. The metric returns 1 if the response contains the reference, and 0 otherwise.