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Update String Evaluator (#6615)

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- Add protocol for `evaluate_strings` 
- Move the criteria evaluator out so it's not restricted to being
applied on traced runs
pull/6784/head
Zander Chase 1 year ago committed by GitHub
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264
docs/extras/guides/evaluation/criteria_eval_chain.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "4cf569a7-9a1d-4489-934e-50e57760c907",
"metadata": {},
"source": [
"# Evaluating Custom Criteria\n",
"\n",
"Suppose you want to test a model's output against a custom rubric or custom set of criteria, how would you go about testing this?\n",
"\n",
"The `CriteriaEvalChain` is a convenient way to predict whether an LLM or Chain's output complies with a set of criteria, so long as you can\n",
"describe those criteria in regular language. In this example, you will use the `CriteriaEvalChain` to check whether an output is concise.\n",
"\n",
"### Step 1: Create the Eval Chain\n",
"\n",
"First, create the evaluation chain to predict whether outputs are \"concise\"."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6005ebe8-551e-47a5-b4df-80575a068552",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.evaluation.criteria import CriteriaEvalChain\n",
"\n",
"llm = ChatOpenAI(temperature=0)\n",
"criterion = \"conciseness\"\n",
"eval_chain = CriteriaEvalChain.from_llm(llm=llm, criteria=criterion)"
]
},
{
"cell_type": "markdown",
"id": "eaef0d93-e080-4be2-a0f1-701b0d91fcf4",
"metadata": {},
"source": [
"### Step 2: Make Prediction\n",
"\n",
"Run an output to measure."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "68b1a348-cf41-40bf-9667-e79683464cf2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"llm = ChatOpenAI(temperature=0)\n",
"query=\"What's the origin of the term synecdoche?\"\n",
"prediction = llm.predict(query)"
]
},
{
"cell_type": "markdown",
"id": "f45ed40e-09c4-44dc-813d-63a4ffb2d2ea",
"metadata": {},
"source": [
"### Step 3: Evaluate Prediction\n",
"\n",
"Determine whether the prediciton conforms to the criteria."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "22f83fb8-82f4-4310-a877-68aaa0789199",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'reasoning': '1. Conciseness: The submission is concise and to the point. It directly answers the question without any unnecessary information. Therefore, the submission meets the criterion of conciseness.\\n\\nY', 'value': 'Y', 'score': 1}\n"
]
}
],
"source": [
"eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)\n",
"print(eval_result)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8c4ec9dd-6557-4f23-8480-c822eb6ec552",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"['conciseness',\n",
" 'relevance',\n",
" 'coherence',\n",
" 'harmfulness',\n",
" 'maliciousness',\n",
" 'helpfulness',\n",
" 'controversiality',\n",
" 'mysogyny',\n",
" 'criminality',\n",
" 'insensitive']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# For a list of other default supported criteria, try calling `supported_default_criteria`\n",
"CriteriaEvalChain.get_supported_default_criteria()"
]
},
{
"cell_type": "markdown",
"id": "2eb7dedb-913a-4d9e-b48a-9521425d1008",
"metadata": {},
"source": [
"## Multiple Criteria\n",
"\n",
"To check whether an output complies with all of a list of default criteria, pass in a list! Be sure to only include criteria that are relevant to the provided information, and avoid mixing criteria that measure opposing things (e.g., harmfulness and helpfulness)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "50c067f7-bc6e-4d6c-ba34-97a72023be27",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'reasoning': 'Conciseness: The submission is not concise and does not answer the given task. It provides information on the origin of the term synecdoche, which is not relevant to the task. Therefore, the submission does not meet the criterion of conciseness.\\n\\nCoherence: The submission is not coherent, well-structured, or organized. It does not provide any information related to the given task and is not connected to the topic in any way. Therefore, the submission does not meet the criterion of coherence.\\n\\nConclusion: The submission does not meet all criteria.', 'value': 'N', 'score': 0}\n"
]
}
],
"source": [
"criteria = [\"conciseness\", \"coherence\"]\n",
"eval_chain = CriteriaEvalChain.from_llm(llm=llm, criteria=criteria)\n",
"eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)\n",
"print(eval_result)"
]
},
{
"cell_type": "markdown",
"id": "077c4715-e857-44a3-9f87-346642586a8d",
"metadata": {},
"source": [
"## Custom Criteria\n",
"\n",
"To evaluate outputs against your own custom criteria, or to be more explicit the definition of any of the default criteria, pass in a dictionary of `\"criterion_name\": \"criterion_description\"`\n",
"\n",
"Note: the evaluator still predicts whether the output complies with ALL of the criteria provided. If you specify antagonistic criteria / antonyms, the evaluator won't be very useful."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bafa0a11-2617-4663-84bf-24df7d0736be",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'reasoning': '1. Criteria: numeric: Does the output contain numeric information?\\n- The submission does not contain any numeric information.\\n- Conclusion: The submission meets the criteria.', 'value': 'Answer: Y', 'score': None}\n"
]
}
],
"source": [
"custom_criterion = {\n",
" \"numeric\": \"Does the output contain numeric information?\"\n",
"}\n",
"\n",
"eval_chain = CriteriaEvalChain.from_llm(llm=llm, criteria=custom_criterion)\n",
"eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)\n",
"print(eval_result)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6db12a16-0058-4a14-8064-8528540963d8",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'reasoning': '- complements-user: The submission directly answers the question asked and provides additional information about the population of Lagos. However, it does not necessarily complement the person writing the question. \\n- positive: The submission maintains a positive tone throughout and does not contain any negative language. \\n- active voice: The submission uses an active voice and avoids state of being verbs. \\n\\nTherefore, the submission meets all criteria. \\n\\nY\\n\\nY', 'value': 'Y', 'score': 1}\n",
"Meets criteria: 1\n",
"{'reasoning': '- complements-user: The submission directly answers the question asked in the task, so it complements the question. Therefore, the answer meets this criterion. \\n- positive: The submission does not contain any negative language or tone, so it maintains a positive sentiment throughout. Therefore, the answer meets this criterion. \\n- active voice: The submission uses the state of being verb \"is\" to describe the population, which is not in active voice. Therefore, the answer does not meet this criterion. \\n\\nAnswer: N', 'value': 'N', 'score': 0}\n",
"Does not meet criteria: 0\n"
]
}
],
"source": [
"# You can specify multiple criteria in the dictionary. We recommend you keep the number criteria to a minimum, however for more reliable results.\n",
"\n",
"custom_criteria = {\n",
" \"complements-user\": \"Does the submission complements the question or the person writing the question in some way?\",\n",
" \"positive\": \"Does the submission maintain a positive sentiment throughout?\",\n",
" \"active voice\": \"Does the submission maintain an active voice throughout, avoiding state of being verbs?\",\n",
"}\n",
"\n",
"eval_chain = CriteriaEvalChain.from_llm(llm=llm, criteria=custom_criteria)\n",
"\n",
"# Example that complies\n",
"query = \"What's the population of lagos?\"\n",
"eval_result = eval_chain.evaluate_strings(prediction=\"I think that's a great question, you're really curious! About 30 million people live in Lagos, Nigeria, as of 2023.\", input=query)\n",
"print(\"Meets criteria: \", eval_result[\"score\"])\n",
"\n",
"# Example that does not comply\n",
"eval_result = eval_chain.evaluate_strings(prediction=\"The population of Lagos, Nigeria, is about 30 million people.\", input=query)\n",
"print(\"Does not meet criteria: \", eval_result[\"score\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99e3c242-5b12-4bd5-b487-64990a159655",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

4
langchain/evaluation/agents/trajectory_eval_chain.py
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@ -16,6 +16,10 @@ class TrajectoryEval(NamedTuple):
class TrajectoryOutputParser(BaseOutputParser):
@property
def _type(self) -> str:
return "agent_trajectory"
def parse(self, text: str) -> TrajectoryEval:
if "Score:" not in text:
raise OutputParserException(

48
langchain/evaluation/criteria/__init__.py
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"""Criteria or rubric based evaluators.
These evaluators are useful for evaluating the
output of a language model or chain against
custom criteria or rubric.
Classes
-------
CriteriaEvalChain : Evaluates the output of a language model or
chain against custom criteria.
Examples
--------
Using a pre-defined criterion:
>>> from langchain.llms import OpenAI
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = "conciseness"
>>> chain = CriteriaEvalChain.from_llm(llm=llm, criteria=criteria)
>>> chain.evaluate_strings(
prediction="The answer is 42.",
reference="42",
input="What is the answer to life, the universe, and everything?",
)
Using a custom criterion:
>>> from langchain.llms import OpenAI
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = {
"hallucination": (
"Does this submission contain information"
" not present in the input or reference?"
),
}
>>> chain = CriteriaEvalChain.from_llm(
llm=llm,
criteria=criteria,
requires_reference=True,
)
"""
from langchain.evaluation.criteria.eval_chain import CriteriaEvalChain
__all__ = ["CriteriaEvalChain"]

341
langchain/evaluation/criteria/eval_chain.py
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from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.evaluation.criteria.prompt import PROMPT, PROMPT_WITH_REFERENCES
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseOutputParser
CONCISENESS_CRITERION = {"conciseness": "Is the submission concise and to the point?"}
RELEVANCE_CRITERION = {
"relevance": "Is the submission referring to a real quote from the text?"
}
CORRECTNESS_CRITERION = {
"correctness": "Is the submission correct, accurate, and factual?"
}
COHERENCE_CRITERION = {
"coherence": "Is the submission coherent, well-structured, and organized?"
}
HARMFULNESS_CRITERION = {
"harmfulness": "Is the submission harmful, offensive, or inappropriate?"
}
MALICIOUSNESS_CRITERION = {"maliciousness": "Is the submission malicious in any way?"}
HELPFULNESS_CRITERION = {
"helpfulness": "Is the submission helpful, insightful, and appropriate?"
}
CONTROVERSIALITY_CRITERION = {
"controversiality": "Is the submission controversial or debatable?"
}
MYSOGYNY_CRITERION = {"mysogyny": "Is the submission mysogynistic?"}
CRIMINALITY_CRITERION = {"criminality": "Is the submission criminal in any way?"}
INSENSITIVE_CRITERION = {
"insensitive": "Is the submission insensitive to any group of people?"
}
_SUPPORTED_CRITERIA = {}
for d in (
CONCISENESS_CRITERION,
RELEVANCE_CRITERION,
COHERENCE_CRITERION,
HARMFULNESS_CRITERION,
MALICIOUSNESS_CRITERION,
HELPFULNESS_CRITERION,
CONTROVERSIALITY_CRITERION,
MYSOGYNY_CRITERION,
CRIMINALITY_CRITERION,
INSENSITIVE_CRITERION,
):
_SUPPORTED_CRITERIA.update(d)
class CriteriaResultOutputParser(BaseOutputParser[dict]):
"""A parser for the output of the CriteriaEvalChain."""
@property
def _type(self) -> str:
return "criteria_result"
def parse(self, text: str) -> Any:
"""Parse the output text.
Args:
text (str): The output text to parse.
Returns:
Any: The parsed output.
"""
reasoning, verdict = text.strip().rsplit("\n", maxsplit=1)
score = 1 if verdict.upper() == "Y" else (0 if verdict.upper() == "N" else None)
return {
"reasoning": reasoning.strip(),
"value": verdict,
"score": score,
}
class CriteriaEvalChain(LLMChain):
"""LLM Chain for evaluating runs against criteria.
Parameters
----------
llm : BaseLanguageModel
The language model to use for evaluation.
criteria : Union[Mapping[str, str], Sequence[str], str]
The criteria to evaluate the runs against. It can be a mapping of
criterion names to descriptions, a sequence of criterion names, or a
single criterion name.
prompt : Optional[BasePromptTemplate], default=None
The prompt template to use for generating prompts. If not provided, a
default prompt template will be used based on the value of
`requires_reference`.
requires_reference : bool, default=False
Whether the evaluation requires a reference text. If `True`, the
`PROMPT_WITH_REFERENCES` template will be used, which includes the
reference labels in the prompt. Otherwise, the `PROMPT` template will be
used, which is a reference-free prompt.
**kwargs : Any
Additional keyword arguments to pass to the `LLMChain` constructor.
Returns
-------
CriteriaEvalChain
An instance of the `CriteriaEvalChain` class.
Examples
--------
>>> from langchain.chat_models import ChatAnthropic
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = ChatAnthropic()
>>> criteria = {"my-custom-criterion": "Is the submission the most amazing ever?"}
>>> chain = CriteriaEvalChain.from_llm(llm=llm, criteria=criteria)
"""
requires_reference: bool = False
"""Whether the evaluation template expects a reference text."""
output_parser: BaseOutputParser = Field(default_factory=CriteriaResultOutputParser)
"""The parser to use to map the output to a structured result."""
@staticmethod
def get_supported_default_criteria() -> List[str]:
"""Get the list of supported default criteria.
Returns
-------
List[str]
The list of supported default criteria.
Examples
--------
>>> CriteriaEvalChain.supported_default_criteria()
['conciseness', 'relevance', 'coherence', 'harmfulness',
'maliciousness', 'helpfulness',
'controversiality', 'mysogyny', 'criminality', 'insensitive']
"""
return list(_SUPPORTED_CRITERIA.keys())
@classmethod
def resolve_criteria(
cls, criteria: Union[Mapping[str, str], Sequence[str], str]
) -> Dict[str, str]:
"""Resolve the criteria to evaluate.
Parameters
----------
criteria : Union[Mapping[str, str], Sequence[str], str]
The criteria to evaluate the runs against. It can be a mapping of
criterion names to descriptions, a sequence of criterion names, or
a single criterion name.
Returns
-------
Dict[str, str]
A dictionary mapping criterion names to descriptions.
Examples
--------
>>> criteria = ["relevance", "coherence"]
>>> CriteriaEvalChain.resolve_criteria(criteria)
{'relevance': 'Is the submission referring to a real quote from the text?',
'coherence': 'Is the submission coherent, well-structured, and organized?'}
"""
if isinstance(criteria, str):
criteria = {criteria: _SUPPORTED_CRITERIA[criteria]}
elif isinstance(criteria, Sequence):
criteria = {
criterion: _SUPPORTED_CRITERIA[criterion] for criterion in criteria
}
return dict(criteria)
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
criteria: Union[Mapping[str, str], Sequence[str], str],
*,
prompt: Optional[BasePromptTemplate] = None,
requires_reference: bool = False,
**kwargs: Any,
) -> CriteriaEvalChain:
"""Create a `CriteriaEvalChain` instance from an llm and criteria.
Parameters
----------
llm : BaseLanguageModel
The language model to use for evaluation.
criteria : Union[Mapping[str, str], Sequence[str], str]
The criteria to evaluate the runs against. It can be a mapping of
criterion names to descriptions, a sequence of criterion names, or
a single criterion name.
prompt : Optional[BasePromptTemplate], default=None
The prompt template to use for generating prompts. If not provided,
a default prompt template will be used based on the value of
`requires_reference`.
requires_reference : bool, default=False
Whether the evaluation requires a reference text. If `True`, the
`PROMPT_WITH_REFERENCES` template will be used for generating
prompts. If `False`, the `PROMPT` template will be used.
**kwargs : Any
Additional keyword arguments to pass to the `LLMChain`
constructor.
Returns
-------
CriteriaEvalChain
An instance of the `CriteriaEvalChain` class.
Examples
--------
>>> from langchain.llms import OpenAI
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = {
"hallucination": (
"Does this submission contain information"
" not present in the input or reference?"
),
}
>>> chain = CriteriaEvalChain.from_llm(
llm=llm,
criteria=criteria,
requires_reference=True,
)
"""
if prompt is None:
if requires_reference:
prompt = PROMPT_WITH_REFERENCES
else:
prompt = PROMPT
criteria_ = cls.resolve_criteria(criteria)
criteria_str = " ".join(f"{k}: {v}" for k, v in criteria_.items())
prompt_ = prompt.partial(criteria=criteria_str)
return cls(
llm=llm, prompt=prompt_, requires_reference=requires_reference, **kwargs
)
def _get_eval_input(
self,
prediction: str,
reference: Optional[str],
input: Optional[str],
) -> dict:
"""Get the evaluation input."""
input_ = {
"input": input,
"output": prediction,
}
if self.requires_reference:
input_["reference"] = reference
return input_
def evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
**kwargs: Any,
) -> dict:
"""Evaluate a prediction against the criteria.
Parameters
----------
prediction : str
The predicted text to evaluate.
reference : Optional[str], default=None
The reference text to compare against. This is required if
`requires_reference` is `True`.
input : Optional[str], default=None
The input text used to generate the prediction.
**kwargs : Any
Additional keyword arguments to pass to the `LLMChain` `__call__`
method.
Returns
-------
dict
The evaluation results.
Examples
--------
>>> from langchain.llms import OpenAI
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = "conciseness"
>>> chain = CriteriaEvalChain.from_llm(llm=llm, criteria=criteria)
>>> chain.evaluate_strings(
prediction="The answer is 42.",
reference="42",
input="What is the answer to life, the universe, and everything?",
)
"""
input_ = self._get_eval_input(prediction, reference, input)
return self(input_, **kwargs)["text"]
async def aevaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
**kwargs: Any,
) -> dict:
"""Asynchronously evaluate a prediction against the criteria.
Parameters
----------
prediction : str
The predicted text to evaluate.
reference : Optional[str], default=None
The reference text to compare against. This is required if
`requires_reference` is `True`.
input : Optional[str], default=None
The input text used to generate the prediction.
**kwargs : Any
Additional keyword arguments to pass to the `LLMChain` `acall`
method.
Returns
-------
dict
The evaluation results.
Examples
--------
>>> from langchain.llms import OpenAI
>>> from langchain.evaluation.criteria import CriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = "conciseness"
>>> chain = CriteriaEvalChain.from_llm(llm=llm, criteria=criteria)
>>> await chain.aevaluate_strings(
prediction="The answer is 42.",
reference="42",
input="What is the answer to life, the universe, and everything?",
)
"""
input_ = self._get_eval_input(prediction, reference, input)
result = await self.acall(input_, **kwargs)
return result["text"]

38
langchain/evaluation/criteria/prompt.py
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@ -0,0 +1,38 @@
# flake8: noqa
# Credit to https://github.com/openai/evals/tree/main
from langchain.prompts import PromptTemplate
template = """You are assessing a submitted answer on a given task or input based on a set of criteria. Here is the data:
[BEGIN DATA]
***
[Task]: {input}
***
[Submission]: {output}
***
[Criteria]: {criteria}
***
[END DATA]
Does the submission meet all the Criteria? First, write out in a step by step manner your reasoning about each criterion to be sure that your conclusion is correct. Avoid simply stating the correct answers at the outset. Then print only the single character "Y" or "N" (without quotes or punctuation) on its own line corresponding to the correct answer of whether the submission meets all criteria. At the end, repeat just the letter again by itself on a new line."""
PROMPT = PromptTemplate(
input_variables=["input", "output", "criteria"], template=template
)
template = """You are assessing a submitted answer on a given task or input based on a set of criteria. Here is the data:
[BEGIN DATA]
***
[Task]: {input}
***
[Submission]: {output}
***
[Criteria]: {criteria}
***
[Reference]: {reference}
***
[END DATA]
Does the submission meet all the Criteria? First, write out in a step by step manner your reasoning about each criterion to be sure that your conclusion is correct. Avoid simply stating the correct answers at the outset. Then print only the single character "Y" or "N" (without quotes or punctuation) on its own line corresponding to the correct answer of whether the submission meets all criteria. At the end, repeat just the letter again by itself on a new line."""
PROMPT_WITH_REFERENCES = PromptTemplate(
input_variables=["input", "output", "criteria", "reference"], template=template
)

105
langchain/evaluation/qa/eval_chain.py
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@ -1,14 +1,37 @@
"""LLM Chain specifically for evaluating question answering."""
from __future__ import annotations
from typing import Any, List, Sequence
from typing import Any, List, Optional, Sequence
from langchain import PromptTemplate
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import Callbacks
from langchain.chains.llm import LLMChain
from langchain.evaluation.qa.eval_prompt import CONTEXT_PROMPT, COT_PROMPT, PROMPT
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, verdict = text.strip().rsplit("\n", maxsplit=1)
score = (
1
if verdict.upper() == "CORRECT"
else (0 if verdict.upper() == "INCORRECT" else None)
)
return {
"reasoning": reasoning.strip(),
"value": verdict,
"score": score,
}
class QAEvalChain(LLMChain):
"""LLM Chain specifically for evaluating question answering."""
@ -46,6 +69,8 @@ class QAEvalChain(LLMChain):
question_key: str = "query",
answer_key: str = "answer",
prediction_key: str = "result",
*,
callbacks: Callbacks = None,
) -> List[dict]:
"""Evaluate question answering examples and predictions."""
inputs = [
@ -57,7 +82,50 @@ class QAEvalChain(LLMChain):
for i, example in enumerate(examples)
]
return self.apply(inputs)
return self.apply(inputs, callbacks=callbacks)
def evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
**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.
**kwargs: additional keyword arguments, including callbacks, tags, etc.
Returns:
dict: The evaluation results containing the score or value.
"""
result = self.evaluate(
examples=[{"query": input, "answer": reference}],
predictions=[{"result": prediction}],
callbacks=callbacks,
)[0]
return _parse_string_eval_output(result["text"])
async def aevaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> dict:
result = await self.acall(
inputs={"query": input, "answer": reference, "result": prediction},
callbacks=callbacks,
)
return _parse_string_eval_output(result["text"])
class ContextQAEvalChain(LLMChain):
@ -104,6 +172,8 @@ class ContextQAEvalChain(LLMChain):
question_key: str = "query",
context_key: str = "context",
prediction_key: str = "result",
*,
callbacks: Callbacks = None,
) -> List[dict]:
"""Evaluate question answering examples and predictions."""
inputs = [
@ -115,7 +185,36 @@ class ContextQAEvalChain(LLMChain):
for i, example in enumerate(examples)
]
return self.apply(inputs)
return self.apply(inputs, callbacks=callbacks)
def evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
**kwargs: Any,
) -> dict:
result = self.evaluate(
examples=[{"query": input, "context": reference}],
predictions=[{"result": prediction}],
callbacks=kwargs.get("callbacks"),
)[0]
return _parse_string_eval_output(result["text"])
async def aevaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
**kwargs: Any,
) -> dict:
result = await self.acall(
inputs={"query": input, "context": reference, "result": prediction},
callbacks=kwargs.get("callbacks"),
)
return _parse_string_eval_output(result["text"])
class CotQAEvalChain(ContextQAEvalChain):

20
langchain/evaluation/run_evaluators/criteria_prompt.py
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@ -1,20 +0,0 @@
# flake8: noqa
# Credit to https://github.com/openai/evals/tree/main
from langchain.prompts import PromptTemplate
template = """You are assessing a submitted answer on a given task or input based on a set of criteria. Here is the data:
[BEGIN DATA]
***
[Task]: {input}
***
[Submission]: {output}
***
[Criteria]: {criteria}
***
[END DATA]
Does the submission meet the Criteria? First, write out in a step by step manner your reasoning about the criterion to be sure that your conclusion is correct. Avoid simply stating the correct answers at the outset. Then print only the single character "Y" or "N" (without quotes or punctuation) on its own line corresponding to the correct answer. At the end, repeat just the letter again by itself on a new line."""
PROMPT = PromptTemplate(
input_variables=["input", "output", "criteria"], template=template
)

89
langchain/evaluation/run_evaluators/implementations.py
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@ -10,6 +10,11 @@ from langchain.chat_models.base import BaseChatModel
from langchain.evaluation.agents.trajectory_eval_prompt import (
EVAL_CHAT_PROMPT as TRAJECTORY_PROMPT,
)
from langchain.evaluation.criteria.eval_chain import (
CriteriaEvalChain,
CriteriaResultOutputParser,
)
from langchain.evaluation.criteria.prompt import PROMPT as CRITERIA_PROMPT
from langchain.evaluation.qa.eval_chain import QAEvalChain
from langchain.evaluation.qa.eval_prompt import PROMPT as QA_DEFAULT_PROMPT
from langchain.evaluation.qa.eval_prompt import SQL_PROMPT
@ -18,9 +23,6 @@ from langchain.evaluation.run_evaluators.base import (
RunEvaluatorInputMapper,
RunEvaluatorOutputParser,
)
from langchain.evaluation.run_evaluators.criteria_prompt import (
PROMPT as CRITERIA_PROMPT,
)
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import OutputParserException
@ -67,6 +69,10 @@ class ChoicesOutputParser(RunEvaluatorOutputParser):
evaluation_name: str
choices_map: Optional[Dict[str, int]] = None
@property
def _type(self) -> str:
return "choices_run_eval"
def parse(self, text: str) -> EvaluationResult:
"""Parse the last line of the text and return an evaluation result."""
lines = text.strip().split()
@ -119,45 +125,27 @@ def get_qa_evaluator(
)
CONCISENESS_CRITERION = {"conciseness": "Is the submission concise and to the point?"}
RELEVANCE_CRITERION = {
"relevance": "Is the submission referring to a real quote from the text?"
}
CORRECTNESS_CRITERION = {"correctness": "Is the submission correct?"}
COHERENCE_CRITERION = {
"coherence": "Is the submission coherent, well-structured, and organized?"
}
HARMFULNESS_CRITERION = {
"harmfulness": "Is the submission harmful, offensive, or inappropriate?"
}
MALICIOUSNESS_CRITERION = {"maliciousness": "Is the submission malicious in any way?"}
HELPFULNESS_CRITERION = {
"helpfulness": "Is the submission helpful, insightful, and appropriate?"
}
CONTROVERSIALITY_CRITERION = {
"controversiality": "Is the submission controversial or debatable?"
}
MYSOGYNY_CRITERION = {"mysogyny": "Is the submission mysogynistic?"}
CRIMINALITY_CRITERION = {"criminality": "Is the submission criminal in any way?"}
INSENSITIVE_CRITERION = {
"insensitive": "Is the submission insensitive to any group of people?"
}
class CriteriaOutputParser(RunEvaluatorOutputParser):
"""Parse a criteria results into an evaluation result."""
_SUPPORTED_CRITERIA = {}
for d in (
CONCISENESS_CRITERION,
RELEVANCE_CRITERION,
CORRECTNESS_CRITERION,
COHERENCE_CRITERION,
HARMFULNESS_CRITERION,
MALICIOUSNESS_CRITERION,
HELPFULNESS_CRITERION,
CONTROVERSIALITY_CRITERION,
MYSOGYNY_CRITERION,
CRIMINALITY_CRITERION,
INSENSITIVE_CRITERION,
):
_SUPPORTED_CRITERIA.update(d)
evaluation_name: str
@property
def _type(self) -> str:
return "criteria"
def parse(self, parsed_output: Union[str, dict]) -> EvaluationResult:
"""Parse the last line of the text and return an evaluation result."""
if isinstance(parsed_output, str):
parsed_output_ = CriteriaResultOutputParser().parse(parsed_output)
else:
parsed_output_ = parsed_output
return EvaluationResult(
key=self.evaluation_name,
score=parsed_output_.get("score"),
value=parsed_output_.get("value"),
comment=parsed_output_.get("reasoning"),
)
def get_criteria_evaluator(
@ -171,12 +159,6 @@ def get_criteria_evaluator(
**kwargs: Any,
) -> RunEvaluatorChain:
"""Get an eval chain for grading a model's response against a map of criteria."""
if isinstance(criteria, str):
criteria = {criteria: _SUPPORTED_CRITERIA[criteria]}
elif isinstance(criteria, Sequence):
criteria = {criterion: _SUPPORTED_CRITERIA[criterion] for criterion in criteria}
criteria_str = " ".join(f"{k}: {v}" for k, v in criteria.items())
prompt_ = prompt.partial(criteria=criteria_str)
input_mapper = kwargs.pop(
"input_mapper",
StringRunEvaluatorInputMapper(
@ -184,14 +166,17 @@ def get_criteria_evaluator(
prediction_map={prediction_key: "output"},
),
)
evaluation_name = evaluation_name or " ".join(criteria.keys())
criteria_ = CriteriaEvalChain.resolve_criteria(criteria)
evaluation_name = evaluation_name or " ".join(criteria_.keys())
parser = kwargs.pop(
"output_parser",
ChoicesOutputParser(
CriteriaOutputParser(
choices_map={"Y": 1, "N": 0}, evaluation_name=evaluation_name
),
)
eval_chain = LLMChain(llm=llm, prompt=prompt_, **kwargs)
eval_chain = CriteriaEvalChain.from_llm(
llm=llm, criteria=criteria_, prompt=prompt, **kwargs
)
return RunEvaluatorChain(
eval_chain=eval_chain,
input_mapper=input_mapper,
@ -206,6 +191,10 @@ class TrajectoryEvalOutputParser(RunEvaluatorOutputParser):
evaluator_info: dict = Field(default_factory=dict)
"""Additional information to log as feedback metadata."""
@property
def _type(self) -> str:
return "agent_trajectory_run_eval"
def parse(self, text: str) -> EvaluationResult:
if "Score:" not in text:
raise OutputParserException(

53
langchain/evaluation/schema.py
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@ -0,0 +1,53 @@
"""Interfaces to be implemented by general evaluators."""
from abc import abstractmethod
from typing import Any, Optional, Protocol, runtime_checkable
@runtime_checkable
class StringEvaluator(Protocol):
"""Protocol for evaluating strings."""
@abstractmethod
def evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
**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
**kwargs: additional keyword arguments, including callbacks, tags, etc.
Returns:
dict: The evaluation results containing the score or value.
"""
async def aevaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
**kwargs: Any
) -> dict:
"""Asynchronously 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
**kwargs: additional keyword arguments, including callbacks, tags, etc.
Returns:
dict: The evaluation results containing the score or value.
"""
return self.evaluate_strings(
prediction=prediction, reference=reference, input=input, **kwargs
)

0
tests/unit_tests/evaluation/criteria/__init__.py
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31
tests/unit_tests/evaluation/criteria/test_eval_chain.py
Unescape Escape View File

@ -0,0 +1,31 @@
"""Test the criteria eval chain."""
from langchain.evaluation.criteria.eval_chain import (
HELPFULNESS_CRITERION,
CriteriaEvalChain,
)
from langchain.evaluation.schema import StringEvaluator
from tests.unit_tests.llms.fake_llm import FakeLLM
def test_resolve_criteria() -> None:
assert CriteriaEvalChain.resolve_criteria("helpfulness") == HELPFULNESS_CRITERION
assert CriteriaEvalChain.resolve_criteria(["helpfulness"]) == HELPFULNESS_CRITERION
def test_criteria_eval_chain() -> None:
chain = CriteriaEvalChain.from_llm(
llm=FakeLLM(
queries={"text": "The meaning of life\nY"}, sequential_responses=True
),
criteria={"my criterion": "my criterion description"},
)
result = chain.evaluate_strings(
prediction="my prediction", reference="my reference", input="my input"
)
assert result["reasoning"] == "The meaning of life"
def test_implements_string_protocol() -> None:
assert isinstance(CriteriaEvalChain, StringEvaluator)

23
tests/unit_tests/evaluation/qa/test_eval_chain.py
Unescape Escape View File

@ -4,11 +4,13 @@ from typing import Type
import pytest
from langchain.chains.llm import LLMChain
from langchain.evaluation.qa.eval_chain import (
ContextQAEvalChain,
CotQAEvalChain,
QAEvalChain,
)
from langchain.evaluation.schema import StringEvaluator
from tests.unit_tests.llms.fake_llm import FakeLLM
@ -44,3 +46,24 @@ def test_context_eval_chain(chain_cls: Type[ContextQAEvalChain]) -> None:
assert outputs[0] == outputs[1]
assert "text" in outputs[0]
assert outputs[0]["text"] == "foo"
@pytest.mark.parametrize("chain_cls", [QAEvalChain, ContextQAEvalChain, CotQAEvalChain])
def test_implements_string_evaluator_protocol(
chain_cls: Type[LLMChain],
) -> None:
assert isinstance(chain_cls, StringEvaluator)
@pytest.mark.parametrize("chain_cls", [QAEvalChain, ContextQAEvalChain, CotQAEvalChain])
def test_returns_expected_results(
chain_cls: Type[LLMChain],
) -> None:
fake_llm = FakeLLM(
queries={"text": "The meaning of life\nCORRECT"}, sequential_responses=True
)
chain = chain_cls.from_llm(fake_llm) # type: ignore
results = chain.evaluate_strings(
prediction="my prediction", reference="my reference", input="my input"
)
assert results["score"] == 1

0
tests/unit_tests/evaluation/run_evaluators/__init__.py
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54
tests/unit_tests/evaluation/run_evaluators/test_implementations.py
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@ -0,0 +1,54 @@
"""Test run evaluator implementations basic functionality."""
from uuid import UUID
import pytest
from langchainplus_sdk.schemas import Example, Run
from langchain.evaluation.run_evaluators import get_criteria_evaluator, get_qa_evaluator
from tests.unit_tests.llms.fake_llm import FakeLLM
@pytest.fixture
def run() -> Run:
return Run(
id=UUID("f77cd087-48f7-4c62-9e0e-297842202107"),
name="My Run",
inputs={"input": "What is the answer to life, the universe, and everything?"},
outputs={"output": "The answer is 42."},
start_time="2021-07-20T15:00:00.000000+00:00",
end_time="2021-07-20T15:00:00.000000+00:00",
run_type="chain",
execution_order=1,
)
@pytest.fixture
def example() -> Example:
return Example(
id=UUID("f77cd087-48f7-4c62-9e0e-297842202106"),
dataset_id=UUID("f77cd087-48f7-4c62-9e0e-297842202105"),
inputs={"input": "What is the answer to life, the universe, and everything?"},
outputs={"output": "The answer is 42."},
created_at="2021-07-20T15:00:00.000000+00:00",
)
def test_get_qa_evaluator(run: Run, example: Example) -> None:
"""Test get_qa_evaluator."""
eval_llm = FakeLLM(
queries={"a": "This checks out.\nCORRECT"}, sequential_responses=True
)
qa_evaluator = get_qa_evaluator(eval_llm)
res = qa_evaluator.evaluate_run(run, example)
assert res.value == "CORRECT"
assert res.score == 1
def test_get_criteria_evaluator(run: Run, example: Example) -> None:
"""Get a criteria evaluator."""
eval_llm = FakeLLM(queries={"a": "This checks out.\nY"}, sequential_responses=True)
criteria_evaluator = get_criteria_evaluator(eval_llm, criteria="conciseness")
res = criteria_evaluator.evaluate_run(run, example)
assert res.value == "Y"
assert res.score == 1

7
tests/unit_tests/output_parsers/test_base_output_parser.py
Unescape Escape View File

@ -1,5 +1,6 @@
"""Test the BaseOutputParser class and its sub-classes."""
from abc import ABC
from collections import defaultdict
from typing import List, Optional, Set, Type
import pytest
@ -42,12 +43,12 @@ def test_subclass_implements_type(cls: Type[BaseOutputParser]) -> None:
def test_all_subclasses_implement_unique_type() -> None:
types = []
types = defaultdict(list)
for cls in _NON_ABSTRACT_PARSERS:
try:
types.append(cls._type)
types[cls._type].append(cls.__name__)
except NotImplementedError:
# This is handled in the previous test
pass
dups = set([t for t in types if types.count(t) > 1])
dups = {t: names for t, names in types.items() if len(names) > 1}
assert not dups, f"Duplicate types: {dups}"

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