/
_answer_relevance.py
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/
_answer_relevance.py
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from __future__ import annotations
import logging
import typing as t
from dataclasses import dataclass, field
import numpy as np
from langchain_core.pydantic_v1 import BaseModel
from ragas.llms.output_parser import RagasoutputParser, get_json_format_instructions
from ragas.llms.prompt import Prompt
from ragas.metrics.base import EvaluationMode, MetricWithEmbeddings, MetricWithLLM
logger = logging.getLogger(__name__)
if t.TYPE_CHECKING:
from langchain_core.callbacks import Callbacks
from ragas.llms.prompt import PromptValue
class AnswerRelevanceClassification(BaseModel):
question: str
noncommittal: int
_output_instructions = get_json_format_instructions(
pydantic_object=AnswerRelevanceClassification
)
_output_parser = RagasoutputParser(pydantic_object=AnswerRelevanceClassification)
QUESTION_GEN = Prompt(
name="question_generation",
instruction="""Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers""",
output_format_instruction=_output_instructions,
examples=[
{
"answer": """Albert Einstein was born in Germany.""",
"context": """Albert Einstein was a German-born theoretical physicist who is widely held to be one of the greatest and most influential scientists of all time""",
"output": AnswerRelevanceClassification.parse_obj(
{
"question": "Where was Albert Einstein born?",
"noncommittal": 0,
}
).dict(),
},
{
"answer": """It can change its skin color based on the temperature of its environment.""",
"context": """A recent scientific study has discovered a new species of frog in the Amazon rainforest that has the unique ability to change its skin color based on the temperature of its environment.""",
"output": AnswerRelevanceClassification.parse_obj(
{
"question": "What unique ability does the newly discovered species of frog have?",
"noncommittal": 0,
}
).dict(),
},
{
"answer": """Everest""",
"context": """The tallest mountain on Earth, measured from sea level, is a renowned peak located in the Himalayas.""",
"output": AnswerRelevanceClassification.parse_obj(
{
"question": "What is the tallest mountain on Earth?",
"noncommittal": 0,
}
).dict(),
},
{
"answer": """I don't know about the groundbreaking feature of the smartphone invented in 2023 as am unaware of information beyond 2022. """,
"context": """In 2023, a groundbreaking invention was announced: a smartphone with a battery life of one month, revolutionizing the way people use mobile technology.""",
"output": AnswerRelevanceClassification.parse_obj(
{
"question": "What was the groundbreaking feature of the smartphone invented in 2023?",
"noncommittal": 1,
}
).dict(),
},
],
input_keys=["answer", "context"],
output_key="output",
output_type="json",
)
@dataclass
class AnswerRelevancy(MetricWithLLM, MetricWithEmbeddings):
"""
Scores the relevancy of the answer according to the given question.
Answers with incomplete, redundant or unnecessary information is penalized.
Score can range from 0 to 1 with 1 being the best.
Attributes
----------
name: string
The name of the metrics
strictness: int
Here indicates the number questions generated per answer.
Ideal range between 3 to 5.
embeddings: Embedding
The langchain wrapper of Embedding object.
E.g. HuggingFaceEmbeddings('BAAI/bge-base-en')
"""
name: str = "answer_relevancy" # type: ignore
evaluation_mode: EvaluationMode = EvaluationMode.qac # type: ignore
question_generation: Prompt = field(default_factory=lambda: QUESTION_GEN)
strictness: int = 3
def calculate_similarity(
self: t.Self, question: str, generated_questions: list[str]
):
assert self.embeddings is not None
question_vec = np.asarray(self.embeddings.embed_query(question)).reshape(1, -1)
gen_question_vec = np.asarray(
self.embeddings.embed_documents(generated_questions)
).reshape(len(generated_questions), -1)
norm = np.linalg.norm(gen_question_vec, axis=1) * np.linalg.norm(
question_vec, axis=1
)
return (
np.dot(gen_question_vec, question_vec.T).reshape(
-1,
)
/ norm
)
def _calculate_score(
self, answers: t.Sequence[AnswerRelevanceClassification], row: t.Dict
) -> float:
question = row["question"]
gen_questions = [answer.question for answer in answers]
committal = np.any([answer.noncommittal for answer in answers])
if all(q == "" for q in gen_questions):
logger.warning(
"Invalid JSON response. Expected dictionary with key 'question'"
)
score = np.nan
else:
cosine_sim = self.calculate_similarity(question, gen_questions)
score = cosine_sim.mean() * int(not committal)
return score
def _create_question_gen_prompt(self, row: t.Dict) -> PromptValue:
ans, ctx = row["answer"], row["contexts"]
return self.question_generation.format(answer=ans, context="\n".join(ctx))
async def _ascore(self, row: t.Dict, callbacks: Callbacks, is_async: bool) -> float:
assert self.llm is not None, "LLM is not set"
prompt = self._create_question_gen_prompt(row)
result = await self.llm.generate(
prompt,
n=self.strictness,
callbacks=callbacks,
is_async=is_async,
)
answers = [
await _output_parser.aparse(result.text, prompt, self.llm)
for result in result.generations[0]
]
if any(answer is None for answer in answers):
return np.nan
answers = [answer for answer in answers if answer is not None]
return self._calculate_score(answers, row)
def adapt(self, language: str, cache_dir: str | None = None) -> None:
assert self.llm is not None, "LLM is not set"
logger.info(f"Adapting AnswerRelevancy metric to {language}")
self.question_generation = self.question_generation.adapt(
language, self.llm, cache_dir
)
def save(self, cache_dir: str | None = None) -> None:
self.question_generation.save(cache_dir)
answer_relevancy = AnswerRelevancy()