add interface for evaluating messages

langchain/evaluation/__init__.py

@@ -1,29 +1,41 @@
-"""Functionality relating to evaluation.
+"""Evaluation chains for grading LLM and Chain outputs.
 
-This module contains off-the-shelf evaluation chains for
-grading the output of LangChain primitives such as LLMs and Chains.
+This module contains off-the-shelf evaluation chains for grading the output of
+LangChain primitives such as language models and chains.
+
+To load an evaluator, you can use the :func:`load_evaluators <langchain.evaluation.loading.load_evaluators>` function with the
+name of the evaluator to load.
+
+To load one of the LangChain HuggingFace datasets, you can use the :func:`load_dataset <langchain.evaluation.loading.load_dataset>` function with the
+name of the dataset to load.
 
 Some common use cases for evaluation include:
 
-- Grading accuracy of a response against ground truth answers: QAEvalChain
-- Comparing the output of two models: PairwiseStringEvalChain
-- Judging the efficacy of an agent's tool usage: TrajectoryEvalChain
-- Checking whether an output complies with a set of criteria: CriteriaEvalChain
+- Grading the accuracy of a response against ground truth answers: :class:`QAEvalChain <langchain.evaluation.qa.eval_chain.QAEvalChain>`
+- Comparing the output of two models: :class:`PairwiseStringEvalChain <langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain>`
+- Judging the efficacy of an agent's tool usage: :class:`TrajectoryEvalChain <langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain>`
+- Checking whether an output complies with a set of criteria: :class:`CriteriaEvalChain <langchain.evaluation.criteria.eval_chain.CriteriaEvalChain>`
 
-This module also contains low level APIs for making more evaluators for your
-custom evaluation task. These include:
-- StringEvaluator: Evaluates an output string against a reference and/or
-    with input context.
-- PairwiseStringEvaluator: Evaluates two strings against each other.
-"""
+This module also contains low-level APIs for creating custom evaluators for
+specific evaluation tasks. These include:
 
+- :class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>`: Evaluates an output string against a reference and/or input context.
+- :class:`PairwiseStringEvaluator <langchain.evaluation.schema.PairwiseStringEvaluator>`: Evaluates two strings against each other.
+
+"""  # noqa: E501
 from langchain.evaluation.agents.trajectory_eval_chain import TrajectoryEvalChain
-from langchain.evaluation.comparison import PairwiseStringEvalChain
+from langchain.evaluation.comparison.eval_chain import PairwiseStringEvalChain
 from langchain.evaluation.criteria.eval_chain import CriteriaEvalChain
+from langchain.evaluation.loading import load_dataset, load_evaluators
 from langchain.evaluation.qa import ContextQAEvalChain, CotQAEvalChain, QAEvalChain
-from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
+from langchain.evaluation.schema import (
+    EvaluatorType,
+    PairwiseStringEvaluator,
+    StringEvaluator,
+)
 
 __all__ = [
+    "EvaluatorType",
     "PairwiseStringEvalChain",
     "QAEvalChain",
     "CotQAEvalChain",
@@ -32,4 +44,6 @@ __all__ = [
     "PairwiseStringEvaluator",
     "TrajectoryEvalChain",
     "CriteriaEvalChain",
+    "load_evaluators",
+    "load_dataset",
 ]

langchain/evaluation/agents/trajectory_eval_chain.py

@@ -7,20 +7,21 @@ chain (LLMChain) to generate the reasoning and scores.
 
 from typing import Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
 
-from pydantic import Field
+from pydantic import Extra, Field
 
+from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
     Callbacks,
 )
-from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chat_models.base import BaseChatModel
 from langchain.evaluation.agents.trajectory_eval_prompt import (
     EVAL_CHAT_PROMPT,
     TOOL_FREE_EVAL_CHAT_PROMPT,
 )
+from langchain.evaluation.schema import EvalChain
 from langchain.schema import AgentAction, BaseOutputParser, OutputParserException
 from langchain.tools.base import BaseTool
 
@@ -69,7 +70,7 @@ class TrajectoryOutputParser(BaseOutputParser):
         return TrajectoryEval(score=int(score_str), reasoning=reasoning)
 
 
-class TrajectoryEvalChain(Chain):
+class TrajectoryEvalChain(EvalChain):
     """A chain for evaluating ReAct style agents.
 
     This chain is used to evaluate ReAct style agents by reasoning about
@@ -123,6 +124,11 @@ class TrajectoryEvalChain(Chain):
     return_reasoning: bool = False
     """Whether to return the reasoning along with the score."""
 
+    class Config:
+        """Configuration for the QAEvalChain."""
+
+        extra = Extra.ignore
+
     @property
     def _tools_description(self) -> str:
         """Get the description of the agent tools.
@@ -186,10 +192,11 @@ The following is the expected answer. Use this to measure correctness:
     @classmethod
     def from_llm(
         cls,
-        llm: BaseChatModel,
+        llm: BaseLanguageModel,
         agent_tools: Optional[Sequence[BaseTool]] = None,
         output_parser: Optional[TrajectoryOutputParser] = None,
         return_reasoning: bool = False,
+        **kwargs: Any,
     ) -> "TrajectoryEvalChain":
         """Create a TrajectoryEvalChain object from a language model chain.
 
@@ -205,6 +212,10 @@ The following is the expected answer. Use this to measure correctness:
         Returns:
             TrajectoryEvalChain: The TrajectoryEvalChain object.
         """
+        if not isinstance(llm, BaseChatModel):
+            raise NotImplementedError(
+                "Only chat models supported by the current trajectory eval"
+            )
         if agent_tools:
             prompt = EVAL_CHAT_PROMPT
         else:
@@ -215,6 +226,7 @@ The following is the expected answer. Use this to measure correctness:
             return_reasoning=return_reasoning,
             eval_chain=eval_chain,
             output_parser=output_parser or TrajectoryOutputParser(),
+            **kwargs,
         )
 
     @property

langchain/evaluation/comparison/eval_chain.py

@@ -3,12 +3,13 @@ from __future__ import annotations
 
 from typing import Any, Optional
 
-from pydantic import Field
+from pydantic import Extra, Field
 
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.llm import LLMChain
 from langchain.evaluation.comparison.prompt import PROMPT, PROMPT_WITH_REFERENCE
+from langchain.evaluation.schema import EvalChain, PairwiseStringEvaluator
 from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import BaseOutputParser
 
@@ -50,7 +51,7 @@ class PairwiseStringResultOutputParser(BaseOutputParser[dict]):
         }
 
 
-class PairwiseStringEvalChain(LLMChain):
+class PairwiseStringEvalChain(PairwiseStringEvaluator, EvalChain, LLMChain):
     """A chain for comparing the output of two models.
 
     Example:
@@ -80,11 +81,16 @@ class PairwiseStringEvalChain(LLMChain):
         default_factory=PairwiseStringResultOutputParser
     )
 
+    class Config:
+        """Configuration for the QAEvalChain."""
+
+        extra = Extra.ignore
+
     @classmethod
     def from_llm(
         cls,
-        *,
         llm: BaseLanguageModel,
+        *,
         prompt: Optional[PromptTemplate] = None,
         require_reference: bool = False,
         **kwargs: Any,
@@ -121,14 +127,23 @@ class PairwiseStringEvalChain(LLMChain):
         return cls(llm=llm, prompt=prompt_, **kwargs)
 
     def _prepare_input(
-        self, prediction: str, prediction_b: str, input: str, reference: Optional[str]
+        self,
+        prediction: str,
+        prediction_b: str,
+        input: Optional[str],
+        reference: Optional[str],
     ) -> dict:
         input_ = {
             "prediction": prediction,
             "prediction_b": prediction_b,
-            "input": input,
         }
-        if reference is not None and "reference" in self.prompt.input_variables:
+        if "input" in self.prompt.input_variables:
+            if not input:
+                raise ValueError("Input is require for this comparison evaluator")
+            input_["input"] = input
+        if "reference" in self.prompt.input_variables:
+            if reference is None:
+                raise ValueError("Reference is required for this comparison evaluator")
             input_["reference"] = reference
         return input_
 
@@ -137,7 +152,7 @@ class PairwiseStringEvalChain(LLMChain):
         *,
         prediction: str,
         prediction_b: str,
-        input: str,
+        input: Optional[str] = None,
         reference: Optional[str] = None,
         callbacks: Callbacks = None,
         **kwargs: Any,
@@ -173,8 +188,8 @@ class PairwiseStringEvalChain(LLMChain):
         *,
         prediction: str,
         prediction_b: str,
-        input: str,
         reference: Optional[str] = None,
+        input: Optional[str] = None,
         callbacks: Callbacks = None,
         **kwargs: Any,
     ) -> dict:

langchain/evaluation/criteria/eval_chain.py

@@ -2,12 +2,13 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
 
-from pydantic import Field
+from pydantic import Extra, Field
 
 from langchain.base_language import BaseLanguageModel
 from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
 from langchain.chains.llm import LLMChain
 from langchain.evaluation.criteria.prompt import PROMPT, PROMPT_WITH_REFERENCES
+from langchain.evaluation.schema import EvalChain, StringEvaluator
 from langchain.schema import BaseOutputParser, BasePromptTemplate
 
 _SUPPORTED_CRITERIA = {
@@ -59,7 +60,7 @@ CRITERIA_TYPE = Union[
 ]
 
 
-class CriteriaEvalChain(LLMChain):
+class CriteriaEvalChain(StringEvaluator, EvalChain, LLMChain):
     """LLM Chain for evaluating runs against criteria.
 
     Parameters
@@ -96,10 +97,30 @@ class CriteriaEvalChain(LLMChain):
     >>> 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."""
+    criteria_names: List[str] = Field(default_factory=list)
+
+    class Config:
+        """Configuration for the QAEvalChain."""
+
+        extra = Extra.ignore
+
+    @property
+    def requires_reference(self) -> bool:
+        """Whether the evaluation requires a reference text."""
+        return "reference" in self.prompt.input_variables
+
+    @property
+    def evaluation_name(self) -> str:
+        """Get the name of the evaluation.
+
+        Returns
+        -------
+        str
+            The name of the evaluation.
+        """
+        return " ".join(self.criteria_names)
 
     @staticmethod
     def get_supported_default_criteria() -> List[str]:
@@ -122,7 +143,7 @@ class CriteriaEvalChain(LLMChain):
     @classmethod
     def resolve_criteria(
         cls,
-        criteria: CRITERIA_TYPE,
+        criteria: Optional[CRITERIA_TYPE],
     ) -> Dict[str, str]:
         """Resolve the criteria to evaluate.
 
@@ -148,6 +169,10 @@ class CriteriaEvalChain(LLMChain):
         {'relevance': 'Is the submission referring to a real quote from the text?',
          'coherence': 'Is the submission coherent, well-structured, and organized?'}
         """  # noqa: E501
+        if criteria is None:
+            return {
+                "helpfulness": _SUPPORTED_CRITERIA["helpfulness"],
+            }
         if isinstance(criteria, str):
             criteria_ = {criteria: _SUPPORTED_CRITERIA[criteria]}
         elif isinstance(criteria, ConstitutionalPrinciple):
@@ -172,7 +197,7 @@ class CriteriaEvalChain(LLMChain):
     def from_llm(
         cls,
         llm: BaseLanguageModel,
-        criteria: CRITERIA_TYPE,
+        criteria: Optional[CRITERIA_TYPE] = None,
         *,
         prompt: Optional[BasePromptTemplate] = None,
         requires_reference: bool = False,
@@ -184,7 +209,7 @@ class CriteriaEvalChain(LLMChain):
         ----------
         llm : BaseLanguageModel
             The language model to use for evaluation.
-        criteria : CRITERIA_TYPE
+        criteria : CRITERIA_TYPE - default=None for "helpfulness"
             The criteria to evaluate the runs against. It can be:
                 -  a mapping of criterion names to descriptions
                 -  a sequence of criterion names
@@ -231,10 +256,14 @@ class CriteriaEvalChain(LLMChain):
             else:
                 prompt = PROMPT
         criteria_ = cls.resolve_criteria(criteria)
+        criteria_names = list(criteria_.keys())
         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
+            llm=llm,
+            prompt=prompt_,
+            criteria_names=criteria_names,
+            **kwargs,
         )
 
     def _get_eval_input(

langchain/evaluation/loading.py

@@ -1,8 +1,107 @@
-from typing import Dict, List
+"""Loading datasets and evaluators."""
+from typing import Any, Dict, List, Optional, Sequence, Type
+
+from langchain.base_language import BaseLanguageModel
+from langchain.chains.base import Chain
+from langchain.chat_models.openai import ChatOpenAI
+from langchain.evaluation.agents.trajectory_eval_chain import TrajectoryEvalChain
+from langchain.evaluation.comparison import PairwiseStringEvalChain
+from langchain.evaluation.criteria.eval_chain import CriteriaEvalChain
+from langchain.evaluation.qa import ContextQAEvalChain, CotQAEvalChain, QAEvalChain
+from langchain.evaluation.schema import EvalChain, EvaluatorType
 
 
 def load_dataset(uri: str) -> List[Dict]:
+    """Load a dataset from the LangChainDatasets HuggingFace org."""
     from datasets import load_dataset
 
     dataset = load_dataset(f"LangChainDatasets/{uri}")
     return [d for d in dataset["train"]]
+
+
+_EVALUATOR_MAP: Dict[EvaluatorType, Type[EvalChain]] = {
+    EvaluatorType.QA: QAEvalChain,
+    EvaluatorType.COT_QA: CotQAEvalChain,
+    EvaluatorType.CONTEXT_QA: ContextQAEvalChain,
+    EvaluatorType.PAIRWISE_STRING: PairwiseStringEvalChain,
+    EvaluatorType.AGENT_TRAJECTORY: TrajectoryEvalChain,
+    EvaluatorType.CRITERIA: CriteriaEvalChain,
+}
+
+
+def _load_evaluator(
+    evaluator: EvaluatorType,
+    *,
+    llm: Optional[BaseLanguageModel] = None,
+    **kwargs: Any,
+) -> Chain:
+    """Load the requested evaluation chain specified by a string.
+
+    Parameters
+    ----------
+    evaluator : EvaluatorType
+        The type of evaluator to load.
+    llm : BaseLanguageModel, optional
+        The language model to use for evaluation, by default None
+    **kwargs : Any
+        Additional keyword arguments to pass to the evaluator.
+
+    Returns
+    -------
+    Chain
+        The loaded evaluation chain.
+
+    Examples
+    --------
+    >>> llm = ChatOpenAI(model="gpt-4", temperature=0)
+    >>> evaluator = _load_evaluator("qa", llm=llm)
+    """
+    llm = llm or ChatOpenAI(model="gpt-4", temperature=0)
+    if evaluator not in _EVALUATOR_MAP:
+        raise ValueError(
+            f"Unknown evaluator type: {evaluator}"
+            f"Valid types are: {list(_EVALUATOR_MAP.keys())}"
+        )
+    return _EVALUATOR_MAP[evaluator].from_llm(llm=llm, **kwargs)
+
+
+def load_evaluators(
+    evaluators: Sequence[EvaluatorType],
+    *,
+    llm: Optional[BaseLanguageModel] = None,
+    config: Optional[dict] = None,
+    **kwargs: Any,
+) -> List[Chain]:
+    """Load evaluators specified by a list of evaluator types.
+
+    Parameters
+    ----------
+    evaluators : Sequence[EvaluatorType]
+        The list of evaluator types to load.
+    llm : BaseLanguageModel, optional
+        The language model to use for evaluation, if none is provided, a default
+        ChatOpenAI gpt-4 model will be used.
+    config : dict, optional
+        A dictionary mapping evaluator types to additional keyword arguments,
+        by default None
+    **kwargs : Any
+        Additional keyword arguments to pass to all evaluators.
+
+    Returns
+    -------
+    List[Chain]
+        The loaded evaluators.
+
+    Examples
+    --------
+    .. code-block:: python
+        from langchain.evaluation import load_evaluators, EvaluatorType
+        evaluators = [EvaluatorType.QA, EvaluatorType.CRITERIA]
+        loaded_evaluators = load_evaluators(evaluators, criteria="helpfulness")
+    """
+    llm = llm or ChatOpenAI(model="gpt-4", temperature=0)
+    loaded = []
+    for evaluator in evaluators:
+        _kwargs = config.get(evaluator, {}) if config else {}
+        loaded.append(_load_evaluator(evaluator, llm=llm, **{**kwargs, **_kwargs}))
+    return loaded

langchain/evaluation/qa/eval_chain.py

@@ -3,11 +3,14 @@ from __future__ import annotations
 
 from typing import Any, List, Optional, Sequence
 
+from pydantic import Extra
+
 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
+from langchain.evaluation.schema import EvalChain, StringEvaluator
 
 
 def _parse_string_eval_output(text: str) -> dict:
@@ -38,9 +41,22 @@ def _parse_string_eval_output(text: str) -> dict:
     }
 
 
-class QAEvalChain(LLMChain):
+class QAEvalChain(LLMChain, StringEvaluator, EvalChain):
     """LLM Chain specifically for evaluating question answering."""
 
+    class Config:
+        """Configuration for the QAEvalChain."""
+
+        extra = Extra.ignore
+
+    @property
+    def evaluation_name(self) -> str:
+        return "correctness"
+
+    @property
+    def requires_reference(self) -> bool:
+        return True
+
     @classmethod
     def from_llm(
         cls, llm: BaseLanguageModel, prompt: PromptTemplate = PROMPT, **kwargs: Any
@@ -134,7 +150,7 @@ class QAEvalChain(LLMChain):
         return _parse_string_eval_output(result["text"])
 
 
-class ContextQAEvalChain(LLMChain):
+class ContextQAEvalChain(LLMChain, StringEvaluator, EvalChain):
     """LLM Chain specifically for evaluating QA w/o GT based on context"""
 
     @classmethod
@@ -146,6 +162,10 @@ class ContextQAEvalChain(LLMChain):
                 f"but got {prompt.input_variables}"
             )
 
+    @property
+    def evaluation_name(self) -> str:
+        return "Contextual Accuracy"
+
     @classmethod
     def from_llm(
         cls,
@@ -226,6 +246,10 @@ class ContextQAEvalChain(LLMChain):
 class CotQAEvalChain(ContextQAEvalChain):
     """LLM Chain specifically for evaluating QA using chain of thought reasoning."""
 
+    @property
+    def evaluation_name(self) -> str:
+        return "COT Contextual Accuracy"
+
     @classmethod
     def from_llm(
         cls, llm: BaseLanguageModel, prompt: PromptTemplate = COT_PROMPT, **kwargs: Any

langchain/evaluation/run_evaluators/__init__.py

@@ -11,6 +11,9 @@ from langchain.evaluation.run_evaluators.implementations import (
     get_qa_evaluator,
     get_trajectory_evaluator,
 )
+from langchain.evaluation.run_evaluators.string_run_evaluator import (
+    StringRunEvaluatorChain,
+)
 
 __all__ = [
     "RunEvaluatorChain",
@@ -21,4 +24,5 @@ __all__ = [
     "get_trajectory_evaluator",
     "StringRunEvaluatorInputMapper",
     "ChoicesOutputParser",
+    "StringRunEvaluatorChain",
 ]

langchain/evaluation/run_evaluators/base.py

@@ -21,6 +21,10 @@ class RunEvaluatorInputMapper:
     def map(self, run: Run, example: Optional[Example] = None) -> Dict[str, Any]:
         """Maps the Run and Optional[Example] to a dictionary"""
 
+    def __call__(self, run: Run, example: Optional[Example] = None) -> Any:
+        """Maps the Run and Optional[Example] to a dictionary"""
+        return self.map(run, example)
+
 
 class RunEvaluatorOutputParser(BaseOutputParser[EvaluationResult]):
     """Parse the output of a run."""

langchain/evaluation/run_evaluators/loading.py

@@ -0,0 +1,69 @@
+""""Loading helpers for run evaluators."""
+
+
+from typing import Any, List, Optional, Sequence, Union
+
+from langchainplus_sdk import RunEvaluator
+
+from langchain.base_language import BaseLanguageModel
+from langchain.chains.base import Chain
+from langchain.evaluation.loading import load_evaluators
+from langchain.evaluation.run_evaluators.string_run_evaluator import (
+    StringRunEvaluatorChain,
+)
+from langchain.evaluation.schema import EvaluatorType, StringEvaluator
+from langchain.tools.base import Tool
+
+
+def load_run_evaluators_for_model(
+    evaluators: Sequence[EvaluatorType],
+    model: Union[Chain, BaseLanguageModel, Tool],
+    *,
+    input_key: Optional[str] = None,
+    prediction_key: Optional[str] = None,
+    reference_key: Optional[str] = None,
+    eval_llm: Optional[BaseLanguageModel] = None,
+    **kwargs: Any,
+) -> List[RunEvaluator]:
+    """Load evaluators specified by a list of evaluator types.
+
+    Parameters
+    ----------
+    evaluators : Sequence[EvaluatorType]
+        The list of evaluator types to load.
+    model : Union[Chain, BaseLanguageModel, Tool]
+        The model to evaluate. Used to infer how to parse the run.
+    input_key : Optional[str], a chain run's input key to map
+        to the evaluator's input
+    prediction_key : Optional[str], the key in the run's outputs to
+        represent the Chain prediction
+    reference_key : Optional[str], the key in the dataset example (row)
+        outputs to represent the reference, or ground-truth label
+    eval_llm : BaseLanguageModel, optional
+        The language model to use for evaluation, if none is provided, a default
+        ChatOpenAI gpt-4 model will be used.
+    **kwargs : Any
+        Additional keyword arguments to pass to all evaluators.
+
+    Returns
+    -------
+    List[RunEvaluator]
+        The loaded Run evaluators.
+    """
+    evaluators_ = load_evaluators(evaluators, llm=eval_llm, **kwargs)
+    run_evaluators = []
+    for evaluator in evaluators_:
+        if isinstance(evaluator, StringEvaluator):
+            run_evaluator = StringRunEvaluatorChain.from_model_and_evaluator(
+                model,
+                evaluator,
+                input_key=input_key,
+                prediction_key=prediction_key,
+                reference_key=reference_key,
+            )
+        else:
+            raise NotImplementedError(
+                f"Run evaluator for {evaluator} is not implemented"
+            )
+        run_evaluators.append(run_evaluator)
+    return run_evaluators

langchain/evaluation/run_evaluators/message_run_evaluator.py

@@ -0,0 +1,138 @@
+"""Run evaluator mapper for message evaluators."""
+
+from abc import abstractmethod
+from typing import Any, Dict, List, Optional, Union, TypedDict
+from langchain.schema import BaseMessage
+
+from langchainplus_sdk.schemas import Example, Run
+from langchain.load.serializable import Serializable
+from langchain.schema import messages_from_dict
+
+
+class RunMapping(TypedDict):
+    prediction: BaseMessage
+    input: List[BaseMessage]
+
+
+class ExampleMapping(TypedDict):
+    reference: BaseMessage
+
+
+class MessageRunMapper(Serializable):
+    """Extract items to evaluate from run object"""
+
+    @property
+    def output_keys(self) -> List[str]:
+        """The keys to extract from the run."""
+        return ["prediction", "input"]
+
+    @abstractmethod
+    def map(self, run: Run) -> RunMapping:
+        """Maps the Run to a dictionary."""
+
+    def __call__(self, run: Run) -> RunMapping:
+        """Maps the Run to a dictionary."""
+        if not run.outputs:
+            raise ValueError(f"Run {run.id} has no outputs to evaluate.")
+        return self.map(run)
+
+
+class MessageExampleMapper(Serializable):
+    """Map an example, or row in the dataset, to the inputs of an evaluation."""
+
+    reference_key: Optional[str] = None
+
+    @property
+    def output_keys(self) -> List[str]:
+        """The keys to extract from the run."""
+        return ["reference"]
+
+    def map(self, example: Example) -> ExampleMapping:
+        """Maps the Example, or dataset row to a dictionary."""
+        if not example.outputs:
+            raise ValueError(
+                f"Example {example.id} has no outputs to use as a reference."
+            )
+        if self.reference_key is None:
+            if len(example.outputs) > 1:
+                raise ValueError(
+                    f"Example {example.id} has multiple outputs, so you must"
+                    " specify a reference_key."
+                )
+            else:
+                output = list(example.outputs.values())[0]
+                return {
+                    "reference": output if isinstance(output, BaseMessage) else messages_from_dict([output])[0]
+                }
+        elif self.reference_key not in example.outputs:
+            raise ValueError(
+                f"Example {example.id} does not have reference key"
+                f" {self.reference_key}."
+            )
+        output = example.outputs[self.reference_key]
+        return {"reference": output if isinstance(output, BaseMessage) else messages_from_dict([output])[0]}
+
+    def __call__(self, example: Example) -> ExampleMapping:
+        """Maps the Run and Example to a dictionary."""
+        if not example.outputs:
+            raise ValueError(
+                f"Example {example.id} has no outputs to use as areference label."
+            )
+        return self.map(example)
+
+
+class ChatModelMessageRunMapper(MessageRunMapper):
+    """Extract items to evaluate from run object."""
+
+    @staticmethod
+    def extract_inputs(inputs: Dict) -> List[BaseMessage]:
+        if not inputs.get("messages"):
+            raise ValueError("Run must have messages as inputs.")
+        if "messages" in inputs:
+            if isinstance(inputs["messages"], list) and inputs["messages"]:
+                if isinstance(inputs["messages"][0], BaseMessage):
+                    return messages_from_dict(inputs["messages"])
+                elif isinstance(inputs["messages"][0], list):
+                    # Runs from Tracer have messages as a list of lists of dicts
+                    return messages_from_dict(inputs["messages"][0])
+        raise ValueError(f"Could not extract messages from inputs: {inputs}")
+
+    @staticmethod
+    def extract_outputs(outputs: Dict) -> BaseMessage:
+        if not outputs.get("generations"):
+            raise ValueError("LLM Run must have generations as outputs.")
+        first_generation: Dict = outputs["generations"][0]
+        if isinstance(first_generation, list):
+            # Runs from Tracer have generations as a list of lists of dicts
+            # Whereas Runs from the API have a list of dicts
+            first_generation = first_generation[0]
+        if "message" in first_generation:
+            return messages_from_dict([first_generation["message"]])[0]
+
+    def map(self, run: Run) -> RunMapping:
+        """Maps the Run to a dictionary."""
+        if run.run_type != "llm":
+            raise ValueError("ChatModel RunMapper only supports LangSmith runs of type llm.")
+        elif not run.outputs:
+            if run.error:
+                raise ValueError(
+                    f"Cannot evaluate errored LLM run {run.id}: {run.error}"
+                )
+            else:
+                raise ValueError(
+                    f"Run {run.id} has no outputs. Cannot evaluate this run."
+                )
+        else:
+            try:
+                inputs = self.extract_inputs(run.inputs)
+            except Exception as e:
+                raise ValueError(
+                    f"Could not parse LM input from run inputs {run.inputs}"
+                ) from e
+            try:
+                output_ = self.extract_outputs(run.outputs)
+            except Exception as e:
+                raise ValueError(
+                    f"Could not parse LM prediction from run outputs {run.outputs}"
+                ) from e
+            return {"input": inputs, "prediction": output_}

langchain/evaluation/run_evaluators/string_run_evaluator.py

@@ -0,0 +1,392 @@
+"""Run evaluator wrapper for string evaluators."""
+from __future__ import annotations
+
+from abc import abstractmethod
+from typing import Any, Dict, List, Optional, Union, Protocol
+
+from langchainplus_sdk import EvaluationResult, RunEvaluator
+from langchainplus_sdk.schemas import Example, Run
+
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForChainRun,
+    CallbackManagerForChainRun,
+)
+from langchain.chains.base import Chain
+from langchain.evaluation.schema import StringEvaluator, MessageEvaluator
+from langchain.load.serializable import Serializable
+from langchain.schema import RUN_KEY, get_buffer_string, messages_from_dict
+from langchain.tools.base import Tool
+
+
+class StringRunMapper(Serializable):
+    """Extract items to evaluate from the run object."""
+
+    @property
+    def output_keys(self) -> List[str]:
+        """The keys to extract from the run."""
+        return ["prediction", "input"]
+
+    @abstractmethod
+    def map(self, run: Run) -> Dict[str, str]:
+        """Maps the Run to a dictionary."""
+
+    def __call__(self, run: Run) -> Dict[str, str]:
+        """Maps the Run to a dictionary."""
+        if not run.outputs:
+            raise ValueError(f"Run {run.id} has no outputs to evaluate.")
+        return self.map(run)
+
+
+class LLMStringRunMapper(StringRunMapper):
+    """Extract items to evaluate from the run object."""
+
+    def serialize_chat_messages(self, messages: List[Dict]) -> str:
+        """Extract the input messages from the run."""
+        chat_messages = messages_from_dict(messages)
+        return get_buffer_string(chat_messages)
+
+    def serialize_inputs(self, inputs: Dict) -> str:
+        if "prompts" in inputs:  # Should we even accept this?
+            input_ = "\n\n".join(inputs["prompts"])
+        elif "prompt" in inputs:
+            input_ = inputs["prompt"]
+        elif "messages" in inputs:
+            input_ = self.serialize_chat_messages(inputs["messages"])
+        else:
+            raise ValueError("LLM Run must have either messages or prompts as inputs.")
+        return input_
+
+    def serialize_outputs(self, outputs: Dict) -> str:
+        if not outputs.get("generations"):
+            raise ValueError("LLM Run must have generations as outputs.")
+        first_generation: Dict = outputs["generations"][0]
+        if isinstance(first_generation, list):
+            # Runs from Tracer have generations as a list of lists of dicts
+            # Whereas Runs from the API have a list of dicts
+            first_generation = first_generation[0]
+        if "message" in first_generation:
+            output_ = self.serialize_chat_messages([first_generation["message"]])
+        else:
+            output_ = first_generation["text"]
+        return output_
+
+    def map(self, run: Run) -> Dict[str, str]:
+        """Maps the Run to a dictionary."""
+        if run.run_type != "llm":
+            raise ValueError("LLM RunMapper only supports LLM runs.")
+        elif not run.outputs:
+            if run.error:
+                raise ValueError(
+                    f"Cannot evaluate errored LLM run {run.id}: {run.error}"
+                )
+            else:
+                raise ValueError(
+                    f"Run {run.id} has no outputs. Cannot evaluate this run."
+                )
+        else:
+            try:
+                inputs = self.serialize_inputs(run.inputs)
+            except Exception as e:
+                raise ValueError(
+                    f"Could not parse LM input from run inputs {run.inputs}"
+                ) from e
+            try:
+                output_ = self.serialize_outputs(run.outputs)
+            except Exception as e:
+                raise ValueError(
+                    f"Could not parse LM prediction from run outputs {run.outputs}"
+                ) from e
+            return {"input": inputs, "prediction": output_}
+
+
+class ChainStringRunMapper(StringRunMapper):
+    """Extract items to evaluate from the run object from a chain."""
+
+    input_key: str
+    """The key from the chain Run's inputs to use as the eval input."""
+    prediction_key: str
+    """The key from the chain Run's outputs to use as the eval prediction."""
+
+    @classmethod
+    def from_chain(
+        cls,
+        model: Chain,
+        input_key: Optional[str] = None,
+        prediction_key: Optional[str] = None,
+    ) -> ChainStringRunMapper:
+        """Create a RunMapper from a chain."""
+        error_messages = []
+        if input_key is None:
+            if len(model.input_keys) > 1:
+                error_messages.append(
+                    f"Chain {model.lc_namespace} has multiple input"
+                    " keys. Please specify 'input_key' when loading."
+                )
+            else:
+                input_key = model.input_keys[0]
+        elif input_key not in model.input_keys:
+            error_messages.append(
+                f"Chain {model.lc_namespace} does not have specified"
+                f" input key {input_key}."
+            )
+        if prediction_key is None:
+            if len(model.output_keys) > 1:
+                error_messages.append(
+                    f"Chain {model.lc_namespace} has multiple"
+                    " output keys. Please specify 'prediction_key' when loading."
+                )
+            else:
+                prediction_key = model.output_keys[0]
+        elif prediction_key not in model.output_keys:
+            error_messages.append(
+                f"Chain {model.lc_namespace} does not have specified"
+                f" prediction_key {prediction_key}."
+            )
+        if error_messages:
+            raise ValueError("\n".join(error_messages))
+        if input_key is None or prediction_key is None:
+            # This should never happen, but mypy doesn't know that.
+            raise ValueError(f"Chain {model.lc_namespace} has no input or output keys.")
+        return cls(input_key=input_key, prediction_key=prediction_key)
+
+    def map(self, run: Run) -> Dict[str, str]:
+        """Maps the Run to a dictionary."""
+        if not run.outputs:
+            raise ValueError(f"Run {run.id} has no outputs to evaluate.")
+        if run.run_type != "chain":
+            raise ValueError("Chain RunMapper only supports Chain runs.")
+        if self.input_key not in run.inputs:
+            raise ValueError(f"Run {run.id} does not have input key {self.input_key}.")
+        elif self.prediction_key not in run.outputs:
+            raise ValueError(
+                f"Run {run.id} does not have prediction key {self.prediction_key}."
+            )
+        else:
+            return {
+                "input": run.inputs[self.input_key],
+                "prediction": run.outputs[self.prediction_key],
+            }
+
+
+class ToolStringRunMapper(StringRunMapper):
+    """Map an input to the tool."""
+
+    def map(self, run: Run) -> Dict[str, str]:
+        if not run.outputs:
+            raise ValueError(f"Run {run.id} has no outputs to evaluate.")
+        return {"input": run.inputs["input"], "prediction": run.outputs["output"]}
+
+
+class StringExampleMapper(Serializable):
+    """Map an example, or row in the dataset, to the inputs of an evaluation."""
+
+    reference_key: Optional[str] = None
+
+    @property
+    def output_keys(self) -> List[str]:
+        """The keys to extract from the run."""
+        return ["reference"]
+
+    def serialize_chat_messages(self, messages: List[Dict]) -> str:
+        """Extract the input messages from the run."""
+        chat_messages = messages_from_dict(messages)
+        return get_buffer_string(chat_messages)
+
+    def map(self, example: Example) -> Dict[str, str]:
+        """Maps the Example, or dataset row to a dictionary."""
+        if not example.outputs:
+            raise ValueError(
+                f"Example {example.id} has no outputs to use as a reference."
+            )
+        if self.reference_key is None:
+            if len(example.outputs) > 1:
+                raise ValueError(
+                    f"Example {example.id} has multiple outputs, so you must"
+                    " specify a reference_key."
+                )
+            else:
+                output = list(example.outputs.values())[0]
+                return {
+                    "reference": output
+                    if type(output) == str
+                    else self.serialize_chat_messages([output])
+                }
+        elif self.reference_key not in example.outputs:
+            raise ValueError(
+                f"Example {example.id} does not have reference key"
+                f" {self.reference_key}."
+            )
+        return {"reference": example.outputs[self.reference_key]}
+
+    def __call__(self, example: Example) -> Dict[str, Any]:
+        """Maps the Run and Example to a dictionary."""
+        if not example.outputs:
+            raise ValueError(
+                f"Example {example.id} has no outputs to use as areference label."
+            )
+        return self.map(example)
+
+
+# TODO(agola11) can make these abstract classes
+class BaseRunMapper(Protocol):
+    def map(self, run: Run) -> Dict[str, Any]: ...
+
+
+class BaseExampleMapper(Protocol):
+    def map(self, example: Example) -> Dict[str, Any]: ...
+
+
+class SimpleRunEvaluatorChain(Chain, RunEvaluator):
+    """Evaluate Run and optional examples."""
+
+    run_mapper: BaseRunMapper
+    """Maps the Run to a dictionary with 'input' and 'prediction' strings."""
+    example_mapper: Optional[BaseExampleMapper] = None
+    """Maps the Example (dataset row) to a dictionary
+    with a 'reference' string."""
+    name: str
+    """The name of the evaluation metric."""
+    evaluator: Union[StringEvaluator, MessageEvaluator]
+    """The evaluation chain."""
+
+    @property
+    def input_keys(self) -> List[str]:
+        return ["run", "example"]
+
+    @property
+    def output_keys(self) -> List[str]:
+        return ["feedback"]
+
+    def _prepare_input(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+        run: Run = inputs["run"]
+        example: Optional[Example] = inputs.get("example")
+        evaluate_inputs = self.run_mapper.map(run)
+        if self.example_mapper:
+            if not example:
+                raise ValueError(
+                    f"Evaluator {self.name} requires an reference"
+                    " example from the dataset,"
+                    f" but none was provided for run {run.id}."
+                )
+            evaluate_inputs.update(self.example_mapper.map(example))
+        return evaluate_inputs
+
+    def _prepare_output(self, output: Dict[str, Any]) -> EvaluationResult:
+        evaluation_result = EvaluationResult(key=self.name, **output)
+        if RUN_KEY in output:
+            # TODO: Not currently surfaced. Update
+            evaluation_result.evaluator_info[RUN_KEY] = output[RUN_KEY]
+        return evaluation_result
+
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        evaluate_inputs = self._prepare_input(inputs)
+        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
+        callbacks = _run_manager.get_child()
+
+        if isinstance(self.evaluator, StringEvaluator):
+            chain_output = self.evaluator.evaluate_strings(
+                **evaluate_inputs,
+                callbacks=callbacks,
+            )
+        elif isinstance(self.evaluator, MessageEvaluator):
+            chain_output = self.evaluator.evaluate_messages(
+                **evaluate_inputs,
+                callbacks=callbacks,
+            )
+        else:
+            raise ValueError("Unsupported evaluator type")
+
+        evaluation_result = self._prepare_output(chain_output)
+        return {"feedback": evaluation_result}
+
+    async def _acall(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: AsyncCallbackManagerForChainRun | None = None,
+    ) -> Dict[str, Any]:
+        evaluate_inputs = self._prepare_input(inputs)
+        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
+        callbacks = _run_manager.get_child()
+
+        if isinstance(self.evaluator, StringEvaluator):
+            chain_output = await self.evaluator.aevaluate_strings(
+                **evaluate_inputs,
+                callbacks=callbacks,
+            )
+        elif isinstance(self.evaluator, MessageEvaluator):
+            chain_output = await self.evaluator.aevaluate_messages(
+                **evaluate_inputs,
+                callbacks=callbacks,
+            )
+        else:
+            raise ValueError("Unsupported evaluator type")
+
+        evaluation_result = self._prepare_output(chain_output)
+        return {"feedback": evaluation_result}
+
+    def evaluate_run(
+        self, run: Run, example: Optional[Example] = None
+    ) -> EvaluationResult:
+        """Evaluate an example."""
+        return self({"run": run, "example": example})["feedback"]
+
+    async def aevaluate_run(
+        self, run: Run, example: Optional[Example] = None
+    ) -> EvaluationResult:
+        """Evaluate an example."""
+        result = await self.acall({"run": run, "example": example})
+        return result["feedback"]
+
+    # TODO: Add ability to load message evaluators
+    @classmethod
+    def from_model_and_evaluator(
+        cls,
+        model: Union[Chain, BaseLanguageModel, Tool],
+        evaluator: Union[StringEvaluator, MessageEvaluator],
+        input_key: Optional[str] = None,
+        prediction_key: Optional[str] = None,
+        reference_key: Optional[str] = None,
+    ) -> SimpleRunEvaluatorChain:
+        """Create a StringRunEvaluatorChain from a model and evaluator."""
+
+        if isinstance(evaluator, StringEvaluator):
+            if isinstance(model, BaseLanguageModel):
+                run_mapper: StringRunMapper = LLMStringRunMapper()
+            elif isinstance(model, Chain):
+                run_mapper = ChainStringRunMapper.from_chain(
+                    model, input_key=input_key, prediction_key=prediction_key
+                )
+            elif isinstance(model, Tool):
+                run_mapper = ToolStringRunMapper()
+            else:
+                raise NotImplementedError(
+                    f"{cls.__name__}.from_model_and_evaluator({type(model)})"
+                    " not yet implemented."
+                    "Expected one of [BaseLanguageModel, Chain, Tool]."
+                )
+            if reference_key is not None or isinstance(model, BaseLanguageModel):
+                example_mapper = StringExampleMapper(reference_key=reference_key)
+            elif evaluator.requires_reference:
+                raise ValueError(
+                    f"Evaluator {evaluator.evaluation_name} requires a reference"
+                    " example from the dataset. Please specify the reference key from"
+                    " amongst the dataset outputs keys."
+                )
+            else:
+                example_mapper = None
+        elif isinstance(evaluator, MessageEvaluator):
+            raise NotImplementedError()
+        else:
+            raise NotImplementedError()
+
+        return cls(
+            name=evaluator.evaluation_name,
+            run_mapper=run_mapper,
+            example_mapper=example_mapper,
+            evaluator=evaluator,
+    )

langchain/evaluation/schema.py

@@ -1,12 +1,56 @@
 """Interfaces to be implemented by general evaluators."""
-from abc import abstractmethod
-from typing import Any, Optional, Protocol, runtime_checkable
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Any, Optional, List
+
+from langchain.base_language import BaseLanguageModel
+from langchain.chains.base import Chain
+from langchain.schema import BaseMessage, get_buffer_string
 
 
-@runtime_checkable
-class StringEvaluator(Protocol):
+class EvaluatorType(str, Enum):
+    """The types of the evaluators."""
+
+    QA = "qa"
+    """Question answering evaluator, which grades answers to questions
+    directly using an LLM."""
+    COT_QA = "cot_qa"
+    """Chain of thought question answering evaluator, which grades
+    answers to questions using
+    chain of thought 'reasoning'."""
+    CONTEXT_QA = "context_qa"
+    """Question answering evaluator that incorporates 'context' in the response."""
+    PAIRWISE_STRING = "pairwise_string"
+    """The pairwise string evaluator, which compares the output of two models."""
+    AGENT_TRAJECTORY = "trajectory"
+    """The agent trajectory evaluator, which grades the agent's intermediate steps."""
+    CRITERIA = "criteria"
+    """The criteria evaluator, which evaluates a model based on a
+    custom set of criteria."""
+
+
+class EvalChain(Chain):
+    """A base class for evaluators that use an LLM."""
+
+    @classmethod
+    @abstractmethod
+    def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> EvalChain:
+        """Create a new evaluator from an LLM."""
+
+
+class StringEvaluator(ABC):
     """Protocol for evaluating strings."""
 
+    @property
+    def evaluation_name(self) -> str:
+        raise NotImplementedError()
+
+    @property
+    def requires_reference(self) -> bool:
+        return False
+
     @abstractmethod
     def evaluate_strings(
         self,
@@ -26,6 +70,10 @@ class StringEvaluator(Protocol):
             **kwargs: additional keyword arguments, including callbacks, tags, etc.
         Returns:
             dict: The evaluation results containing the score or value.
+                It is recommended that the dictionary contain the following keys:
+                    - score: the score of the evaluation, if applicable.
+                    - value: the string value of the evaluation, if applicable.
+                    - reasoning: the reasoning for the evaluation, if applicable.
         """
 
     async def aevaluate_strings(
@@ -47,6 +95,10 @@ class StringEvaluator(Protocol):
             **kwargs: additional keyword arguments, including callbacks, tags, etc.
         Returns:
             dict: The evaluation results containing the score or value.
+                It is recommended that the dictionary contain the following keys:
+                    - score: the score of the evaluation, if applicable.
+                    - value: the string value of the evaluation, if applicable.
+                    - reasoning: the reasoning for the evaluation, if applicable.
         """
         raise NotImplementedError(
             f"{self.__class__.__name__} hasn't implemented an "
@@ -54,8 +106,114 @@ class StringEvaluator(Protocol):
         )
 
 
-@runtime_checkable
-class PairwiseStringEvaluator(Protocol):
+class MessageEvaluator(ABC):
+    """Protocol for evaluating messages."""
+
+    @property
+    def evaluation_name(self) -> str:
+        raise NotImplementedError()
+
+    @property
+    def requires_reference(self) -> bool:
+        return False
+
+    @abstractmethod
+    def evaluate_messages(
+        self,
+        *,
+        prediction: BaseMessage,
+        reference: Optional[BaseMessage] = None,
+        input: Optional[List[BaseMessage]] = None,
+        **kwargs: Any,
+    ) -> dict:
+        """Evaluate Chain or LLM output, based on optional input and label.
+
+        Args:
+            prediction (BaseMessage): the prediction to evaluate.
+            reference (Optional[BaseMessage], optional): the reference label
+                to evaluate against.
+            input (Optional[List[BaseMessage]], 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.
+                It is recommended that the dictionary contain the following keys:
+                    - score: the score of the evaluation, if applicable.
+                    - value: the string value of the evaluation, if applicable.
+                    - reasoning: the reasoning for the evaluation, if applicable.
+        """
+
+    async def aevaluate_messages(
+        self,
+        *,
+        prediction: BaseMessage,
+        reference: Optional[BaseMessage] = None,
+        input: Optional[List[BaseMessage]] = None,
+        **kwargs: Any,
+    ) -> dict:
+        """Asynchronously evaluate Chain or LLM output, based on optional
+          input and label.
+
+        Args:
+            prediction (BaseMessage): the prediction to evaluate.
+            reference (Optional[BaseMessage], optional): the reference label
+                 to evaluate against.
+            input (Optional[List[BaseMessage]], 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.
+                It is recommended that the dictionary contain the following keys:
+                    - score: the score of the evaluation, if applicable.
+                    - value: the string value of the evaluation, if applicable.
+                    - reasoning: the reasoning for the evaluation, if applicable.
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} hasn't implemented an "
+            "async aevaluate_messages method."
+        )
+
+
+
+# TODO(agola11): move this out of schema
+class SimpleMessageEvaluator(MessageEvaluator):
+    """Simple implementation of MessageEvaluator that delegates to a StringEvaluator."""
+
+    def __init__(self, string_evaluator: StringEvaluator):
+        self.string_evaluator = string_evaluator
+
+    def evaluate_messages(
+        self,
+        *,
+        prediction: BaseMessage,
+        reference: Optional[BaseMessage] = None,
+        input: Optional[List[BaseMessage]] = None,
+        **kwargs: Any,
+    ) -> dict:
+        return self.string_evaluator.evaluate_strings(
+            prediction=get_buffer_string([prediction]),
+            reference=get_buffer_string([reference]) if reference else None,
+            input=get_buffer_string(input) if input else None,
+            **kwargs,
+        )
+
+    async def aevaluate_messages(
+        self,
+        *,
+        prediction: BaseMessage,
+        reference: Optional[BaseMessage] = None,
+        input: Optional[List[BaseMessage]] = None,
+        **kwargs: Any,
+    ) -> dict:
+        return await self.string_evaluator.aevaluate_strings(
+            prediction=get_buffer_string([prediction]),
+            reference=get_buffer_string([reference]) if reference else None,
+            input=get_buffer_string(input) if input else None,
+            **kwargs,
+        )
+
+
+class PairwiseStringEvaluator(ABC):
     """A protocol for comparing the output of two models."""
 
     @abstractmethod
@@ -86,6 +244,7 @@ class PairwiseStringEvaluator(Protocol):
 
     async def aevaluate_string_pairs(
         self,
+        *,
         prediction: str,
         prediction_b: str,
         reference: Optional[str] = None,

langchain/schema/messages.py

@@ -168,7 +168,7 @@ def _message_from_dict(message: dict) -> BaseMessage:
     elif _type == "chat":
         return ChatMessage(**message["data"])
     else:
-        raise ValueError(f"Got unexpected type: {_type}")
+        raise ValueError(f"Got unexpected message type: {_type}")
 
 
 def messages_from_dict(messages: List[dict]) -> List[BaseMessage]:

tests/unit_tests/evaluation/agents/test_eval_chain.py

@@ -1,13 +1,15 @@
 """Test agent trajectory evaluation chain."""
 
-from typing import List, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import pytest
+from pydantic import Field
 
+from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.evaluation.agents.trajectory_eval_chain import TrajectoryEvalChain
-from langchain.schema import AgentAction
+from langchain.schema import AgentAction, BaseMessage
 from langchain.tools.base import tool
-from tests.unit_tests.llms.fake_llm import FakeLLM
+from tests.unit_tests.llms.fake_chat_model import FakeChatModel
 
 
 @pytest.fixture
@@ -30,10 +32,31 @@ def foo(bar: str) -> str:
     return bar
 
 
+class _FakeTrajectoryChatModel(FakeChatModel):
+    queries: Dict = Field(default_factory=dict)
+    sequential_responses: Optional[bool] = False
+    response_index: int = 0
+
+    def _call(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        if self.sequential_responses:
+            response = self.queries[list(self.queries.keys())[self.response_index]]
+            self.response_index = self.response_index + 1
+            return response
+        else:
+            prompt = messages[0].content
+            return self.queries[prompt]
+
+
 def test_trajectory_eval_chain(
     intermediate_steps: List[Tuple[AgentAction, str]]
 ) -> None:
-    llm = FakeLLM(
+    llm = _FakeTrajectoryChatModel(
         queries={
             "a": "Trajectory good\nScore: 5",
             "b": "Trajectory not good\nScore: 1",
@@ -61,7 +84,7 @@ def test_trajectory_eval_chain(
 def test_trajectory_eval_chain_no_tools(
     intermediate_steps: List[Tuple[AgentAction, str]]
 ) -> None:
-    llm = FakeLLM(
+    llm = _FakeTrajectoryChatModel(
         queries={
             "a": "Trajectory good\nScore: 5",
             "b": "Trajectory not good\nScore: 1",
@@ -85,7 +108,7 @@ def test_trajectory_eval_chain_no_tools(
 
 
 def test_old_api_works(intermediate_steps: List[Tuple[AgentAction, str]]) -> None:
-    llm = FakeLLM(
+    llm = _FakeTrajectoryChatModel(
         queries={
             "a": "Trajectory good\nScore: 5",
             "b": "Trajectory not good\nScore: 1",

tests/unit_tests/evaluation/criteria/test_eval_chain.py

@@ -32,4 +32,4 @@ def test_criteria_eval_chain() -> None:
 
 
 def test_implements_string_protocol() -> None:
-    assert isinstance(CriteriaEvalChain, StringEvaluator)
+    assert issubclass(CriteriaEvalChain, StringEvaluator)

tests/unit_tests/evaluation/qa/test_eval_chain.py

@@ -52,7 +52,7 @@ def test_context_eval_chain(chain_cls: Type[ContextQAEvalChain]) -> None:
 def test_implements_string_evaluator_protocol(
     chain_cls: Type[LLMChain],
 ) -> None:
-    assert isinstance(chain_cls, StringEvaluator)
+    assert issubclass(chain_cls, StringEvaluator)
 
 
 @pytest.mark.parametrize("chain_cls", [QAEvalChain, ContextQAEvalChain, CotQAEvalChain])

tests/unit_tests/evaluation/run_evaluators/test_loading.py

@@ -0,0 +1,114 @@
+"""Test the loading function for evalutors."""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
+from langchain.evaluation.loading import load_evaluators
+from langchain.evaluation.run_evaluators.string_run_evaluator import (
+    StringRunEvaluatorChain,
+)
+from langchain.evaluation.schema import StringEvaluator
+from tests.unit_tests.chains.test_base import FakeChain
+from tests.unit_tests.llms.fake_chat_model import FakeChatModel
+from tests.unit_tests.llms.fake_llm import FakeLLM
+
+
+@pytest.mark.parametrize("evaluator_type", ["qa", "cot_qa", "context_qa", "criteria"])
+def test_load_string_run_evaluators_with_llm(evaluator_type: str) -> None:
+    """Test loading evaluators."""
+    fake_llm = FakeLLM(
+        queries={"text": "The meaning of life\nCORRECT"}, sequential_responses=True
+    )
+    evaluator = load_evaluators([evaluator_type], llm=fake_llm)[0]  # type: ignore
+    if not isinstance(evaluator, StringEvaluator):
+        raise ValueError("Evaluator is not a string evaluator")
+    model = FakeLLM(queries={"text": "Foo output"}, sequential_responses=True)
+    kwargs = {}
+    if evaluator.requires_reference:
+        kwargs["reference_key"] = "generations"
+    run_evaluator = StringRunEvaluatorChain.from_model_and_evaluator(
+        model, evaluator, **kwargs
+    )
+    callback = RunCollectorCallbackHandler()
+    model.predict("Foo input", callbacks=[callback])
+    run = callback.traced_runs[0]
+    example = MagicMock()
+    example.inputs = {}
+    example.outputs = {"generations": "Foo output"}
+    result = run_evaluator._prepare_input({"run": run, "example": example})
+    assert result["input"] == "Foo input"
+    assert result["prediction"] == "Foo output"
+    if evaluator.requires_reference:
+        assert "reference" in result
+        assert result["reference"] == "Foo output"
+
+
+@pytest.mark.parametrize("evaluator_type", ["qa", "cot_qa", "context_qa", "criteria"])
+def test_load_string_run_evaluators_with_chat_model(evaluator_type: str) -> None:
+    """Test loading evaluators."""
+    fake_llm = FakeLLM(
+        queries={"text": "The meaning of life\nCORRECT"}, sequential_responses=True
+    )
+    evaluator = load_evaluators([evaluator_type], llm=fake_llm)[0]  # type: ignore
+    if not isinstance(evaluator, StringEvaluator):
+        raise ValueError("Evaluator is not a string evaluator")
+    model = FakeChatModel()
+    kwargs = {}
+    if evaluator.requires_reference:
+        kwargs["reference_key"] = "generations"
+    run_evaluator = StringRunEvaluatorChain.from_model_and_evaluator(
+        model, evaluator, **kwargs
+    )
+    callback = RunCollectorCallbackHandler()
+    model.predict("Foo input", callbacks=[callback])
+    run = callback.traced_runs[0]
+    example = MagicMock()
+    example.inputs = {}
+    example.outputs = {"generations": "Another fake response"}
+    result = run_evaluator._prepare_input({"run": run, "example": example})
+    assert result["input"] == "Human: Foo input"
+    assert result["prediction"] == "fake response"
+    if evaluator.requires_reference:
+        assert "reference" in result
+        assert result["reference"] == "Another fake response"
+
+
+@pytest.mark.parametrize("evaluator_type", ["qa", "cot_qa", "context_qa", "criteria"])
+def test_load_string_run_evaluators_with_chain(evaluator_type: str) -> None:
+    model = FakeChain(
+        the_input_keys=["an_input", "another_input"],
+    )
+    fake_llm = FakeChatModel()
+    evaluator = load_evaluators([evaluator_type], llm=fake_llm)[0]  # type: ignore
+    if not isinstance(evaluator, StringEvaluator):
+        raise ValueError("Evaluator is not a string evaluator")
+    # No input key
+    with pytest.raises(ValueError, match="multiple input keys"):
+        StringRunEvaluatorChain.from_model_and_evaluator(model, evaluator)
+    with pytest.raises(ValueError, match="does not have specified"):
+        StringRunEvaluatorChain.from_model_and_evaluator(
+            model, evaluator, input_key="some_input"
+        )
+    kwargs = {}
+    if evaluator.requires_reference:
+        kwargs["reference_key"] = "label_column"
+    run_evaluator = StringRunEvaluatorChain.from_model_and_evaluator(
+        model, evaluator, input_key="an_input", **kwargs
+    )
+    callback = RunCollectorCallbackHandler()
+    model(
+        {"an_input": "Foo input", "another_input": "Another fake response"},
+        callbacks=[callback],
+    )
+    run = callback.traced_runs[0]
+    example = MagicMock()
+    example.inputs = {}
+    example.outputs = {"label_column": "Another fake response"}
+    result = run_evaluator._prepare_input({"run": run, "example": example})
+    assert result["input"] == "Foo input"
+    assert result["prediction"] == "baz"
+    if evaluator.requires_reference:
+        assert "reference" in result
+        assert result["reference"] == "Another fake response"

tests/unit_tests/evaluation/test_loading.py

@@ -0,0 +1,31 @@
+"""Test the loading function for evalutors."""
+
+import pytest
+
+from langchain.evaluation.loading import EvaluatorType, load_evaluators
+from langchain.evaluation.schema import StringEvaluator
+from tests.unit_tests.llms.fake_chat_model import FakeChatModel
+from tests.unit_tests.llms.fake_llm import FakeLLM
+
+
+@pytest.mark.parametrize("evaluator_type", EvaluatorType)
+def test_load_evaluators(evaluator_type: EvaluatorType) -> None:
+    """Test loading evaluators."""
+    fake_llm = FakeChatModel()
+    load_evaluators([evaluator_type], llm=fake_llm)
+
+    # Test as string
+    load_evaluators([evaluator_type.value], llm=fake_llm)  # type: ignore
+
+
+def test_criteria_eval_chain_requires_reference() -> None:
+    """Test loading evaluators."""
+    fake_llm = FakeLLM(
+        queries={"text": "The meaning of life\nCORRECT"}, sequential_responses=True
+    )
+    evaluator = load_evaluators(
+        [EvaluatorType.CRITERIA], llm=fake_llm, requires_reference=True
+    )[0]
+    if not isinstance(evaluator, StringEvaluator):
+        raise ValueError("Evaluator is not a string evaluator")
+    assert evaluator.requires_reference