"""LLM Chains for evaluating question answering."""
from __future__ import annotations
import re
import string
from typing import Any, List, Optional, Sequence, Tuple
from langchain_core.callbacks.manager import Callbacks
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import PromptTemplate
from langchain_core.pydantic_v1 import Extra
from langchain.chains.llm import LLMChain
from langchain.evaluation.qa.eval_prompt import CONTEXT_PROMPT, COT_PROMPT, PROMPT
from langchain.evaluation.schema import LLMEvalChain, StringEvaluator
from langchain.schema import RUN_KEY
def _get_score(text: str) -> Optional[Tuple[str, int]]:
match = re.search(r"grade:\s*(correct|incorrect)", text.strip(), re.IGNORECASE)
if match:
if match.group(1).upper() == "CORRECT":
return "CORRECT", 1
elif match.group(1).upper() == "INCORRECT":
return "INCORRECT", 0
try:
first_word = (
text.strip().split()[0].translate(str.maketrans("", "", string.punctuation))
)
if first_word.upper() == "CORRECT":
return "CORRECT", 1
elif first_word.upper() == "INCORRECT":
return "INCORRECT", 0
last_word = (
text.strip()
.split()[-1]
.translate(str.maketrans("", "", string.punctuation))
)
if last_word.upper() == "CORRECT":
return "CORRECT", 1
elif last_word.upper() == "INCORRECT":
return "INCORRECT", 0
except IndexError:
pass
return None
def _parse_string_eval_output(text: str) -> dict:
"""Parse the output text.
Args:
text (str): The output text to parse.
Returns:
Any: The parsed output.
"""
reasoning = text.strip()
parsed_scores = _get_score(reasoning)
if parsed_scores is None:
value, score = None, None
else:
value, score = parsed_scores
return {
"reasoning": reasoning,
"value": value,
"score": score,
}
[docs]class QAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
"""LLM Chain for evaluating question answering."""
output_key: str = "results" #: :meta private:
class Config:
"""Configuration for the QAEvalChain."""
extra = Extra.ignore
[docs] @classmethod
def is_lc_serializable(cls) -> bool:
return False
@property
def evaluation_name(self) -> str:
return "correctness"
@property
def requires_reference(self) -> bool:
return True
@property
def requires_input(self) -> bool:
return True
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
) -> QAEvalChain:
"""Load QA Eval Chain from LLM.
Args:
llm (BaseLanguageModel): the base language model to use.
prompt (PromptTemplate): A prompt template containing the input_variables:
'input', 'answer' and 'result' that will be used as the prompt
for evaluation.
Defaults to PROMPT.
**kwargs: additional keyword arguments.
Returns:
QAEvalChain: the loaded QA eval chain.
"""
prompt = prompt or PROMPT
expected_input_vars = {"query", "answer", "result"}
if expected_input_vars != set(prompt.input_variables):
raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt.input_variables}"
)
return cls(llm=llm, prompt=prompt, **kwargs)
[docs] def evaluate(
self,
examples: Sequence[dict],
predictions: Sequence[dict],
question_key: str = "query",
answer_key: str = "answer",
prediction_key: str = "result",
*,
callbacks: Callbacks = None,
) -> List[dict]:
"""Evaluate question answering examples and predictions."""
inputs = [
{
"query": example[question_key],
"answer": example[answer_key],
"result": predictions[i][prediction_key],
}
for i, example in enumerate(examples)
]
return self.apply(inputs, callbacks=callbacks)
def _prepare_output(self, result: dict) -> dict:
parsed_result = _parse_string_eval_output(result[self.output_key])
if RUN_KEY in result:
parsed_result[RUN_KEY] = result[RUN_KEY]
return parsed_result
def _evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
"""Evaluate Chain or LLM output, based on optional input and label.
Args:
prediction (str): the LLM or chain prediction to evaluate.
reference (Optional[str], optional): the reference label
to evaluate against.
input (Optional[str], optional): the input to consider during evaluation
callbacks (Callbacks, optional): the callbacks to use for tracing.
include_run_info (bool, optional): whether to include run info in the
returned results.
**kwargs: additional keyword arguments, including callbacks, tags, etc.
Returns:
dict: The evaluation results containing the score or value.
"""
result = self(
{
"query": input,
"answer": reference,
"result": prediction,
},
callbacks=callbacks,
include_run_info=include_run_info,
)
return self._prepare_output(result)
async def _aevaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
result = await self.acall(
inputs={"query": input, "answer": reference, "result": prediction},
callbacks=callbacks,
include_run_info=include_run_info,
)
return self._prepare_output(result)
[docs]class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
"""LLM Chain for evaluating QA w/o GT based on context"""
[docs] @classmethod
def is_lc_serializable(cls) -> bool:
return False
@property
def requires_reference(self) -> bool:
"""Whether the chain requires a reference string."""
return True
@property
def requires_input(self) -> bool:
"""Whether the chain requires an input string."""
return True
class Config:
"""Configuration for the QAEvalChain."""
extra = Extra.ignore
@classmethod
def _validate_input_vars(cls, prompt: PromptTemplate) -> None:
expected_input_vars = {"query", "context", "result"}
if expected_input_vars != set(prompt.input_variables):
raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt.input_variables}"
)
@property
def evaluation_name(self) -> str:
return "Contextual Accuracy"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
) -> ContextQAEvalChain:
"""Load QA Eval Chain from LLM.
Args:
llm (BaseLanguageModel): the base language model to use.
prompt (PromptTemplate): A prompt template containing the input_variables:
'query', 'context' and 'result' that will be used as the prompt
for evaluation.
Defaults to PROMPT.
**kwargs: additional keyword arguments.
Returns:
ContextQAEvalChain: the loaded QA eval chain.
"""
prompt = prompt or CONTEXT_PROMPT
cls._validate_input_vars(prompt)
return cls(llm=llm, prompt=prompt, **kwargs)
[docs] def evaluate(
self,
examples: List[dict],
predictions: List[dict],
question_key: str = "query",
context_key: str = "context",
prediction_key: str = "result",
*,
callbacks: Callbacks = None,
) -> List[dict]:
"""Evaluate question answering examples and predictions."""
inputs = [
{
"query": example[question_key],
"context": example[context_key],
"result": predictions[i][prediction_key],
}
for i, example in enumerate(examples)
]
return self.apply(inputs, callbacks=callbacks)
def _prepare_output(self, result: dict) -> dict:
parsed_result = _parse_string_eval_output(result[self.output_key])
if RUN_KEY in result:
parsed_result[RUN_KEY] = result[RUN_KEY]
return parsed_result
def _evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
result = self(
{
"query": input,
"context": reference,
"result": prediction,
},
callbacks=callbacks,
include_run_info=include_run_info,
)
return self._prepare_output(result)
async def _aevaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
result = await self.acall(
inputs={"query": input, "context": reference, "result": prediction},
callbacks=callbacks,
include_run_info=include_run_info,
)
return self._prepare_output(result)
[docs]class CotQAEvalChain(ContextQAEvalChain):
"""LLM Chain for evaluating QA using chain of thought reasoning."""
[docs] @classmethod
def is_lc_serializable(cls) -> bool:
return False
@property
def evaluation_name(self) -> str:
return "COT Contextual Accuracy"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
) -> CotQAEvalChain:
"""Load QA Eval Chain from LLM."""
prompt = prompt or COT_PROMPT
cls._validate_input_vars(prompt)
return cls(llm=llm, prompt=prompt, **kwargs)