Add Trajectory Eval RunEvaluator (#6449)

langchain/evaluation/run_evaluators/implementations.py

@@ -1,11 +1,15 @@
-from typing import Any, Dict, Mapping, Optional, Sequence, Union
+from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
 
 from langchainplus_sdk.evaluation import EvaluationResult
-from langchainplus_sdk.schemas import Example, Run
+from langchainplus_sdk.schemas import Example, Run, RunTypeEnum
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
+from langchain.chat_models.base import BaseChatModel
+from langchain.evaluation.agents.trajectory_eval_prompt import (
+    EVAL_CHAT_PROMPT as TRAJECTORY_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
@@ -17,7 +21,10 @@ from langchain.evaluation.run_evaluators.base import (
 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
+from langchain.tools.base import BaseTool
 
 _QA_PROMPTS = {
     "qa": QA_DEFAULT_PROMPT,
@@ -28,18 +35,13 @@ _QA_PROMPTS = {
 class StringRunEvaluatorInputMapper(RunEvaluatorInputMapper, BaseModel):
     """Maps the Run and Optional[Example] to a dictionary."""
 
-    prediction_map: Mapping[str, str]
+    prediction_map: Dict[str, str]
     """Map from run outputs to the evaluation inputs."""
-    input_map: Mapping[str, str]
+    input_map: Dict[str, str]
     """Map from run inputs to the evaluation inputs."""
-    answer_map: Optional[Mapping[str, str]] = None
+    answer_map: Optional[Dict[str, str]] = None
     """Map from example outputs to the evaluation inputs."""
 
-    class Config:
-        """Pydantic config."""
-
-        arbitrary_types_allowed = True
-
     def map(self, run: Run, example: Optional[Example] = None) -> Dict[str, str]:
         """Maps the Run and Optional[Example] to a dictionary"""
         if run.outputs is None:
@@ -166,7 +168,7 @@ def get_criteria_evaluator(
     *,
     input_key: str = "input",
     prediction_key: str = "output",
-    prompt: PromptTemplate = CRITERIA_PROMPT,
+    prompt: BasePromptTemplate = CRITERIA_PROMPT,
     evaluation_name: Optional[str] = None,
     **kwargs: Any,
 ) -> RunEvaluatorChain:
@@ -198,3 +200,125 @@ def get_criteria_evaluator(
         output_parser=parser,
         **kwargs,
     )
+
+
+class TrajectoryEvalOutputParser(RunEvaluatorOutputParser):
+    evaluation_name: str = "Agent Trajectory"
+    """The name assigned to the evaluation feedback."""
+    evaluator_info: dict = Field(default_factory=dict)
+    """Additional information to log as feedback metadata."""
+
+    def parse(self, text: str) -> EvaluationResult:
+        if "Score:" not in text:
+            raise OutputParserException(
+                f"Could not find score in model eval output: {text}"
+            )
+
+        reasoning, score_str = text.split("Score: ")
+
+        reasoning, score_str = reasoning.strip(), score_str.strip()
+
+        score_str = next(
+            (char for char in score_str if char.isdigit()), "0"
+        )  # Scan for first digit
+
+        if not 1 <= int(score_str) <= 5:
+            raise OutputParserException(
+                f"Score is not a digit in the range 1-5: {text}"
+            )
+
+        return EvaluationResult(
+            key=self.evaluation_name,
+            score=int(score_str),
+            comment=reasoning,
+            evaluator_info=self.evaluator_info,
+        )
+
+
+class TrajectoryInputMapper(RunEvaluatorInputMapper, BaseModel):
+    """Maps the Run and Optional[Example] to a dictionary."""
+
+    tool_descriptions: List[str]
+    """The descriptions for each of the tools available to the agent."""
+    agent_input_key: str = "input"
+    """The key to load from the agent executor's run input dictionary."""
+    agent_output_key: str = "output"
+    """The key to load from the agent executor's run output dictionary."""
+    tool_input_key: str = "input"
+    """The key to load from the tool executor's run input dictionary."""
+    tool_output_key: str = "output"
+    """The key to load from the tool executor's run output dictionary."""
+
+    def map(self, run: Run, example: Optional[Example] = None) -> Dict[str, str]:
+        """Maps the Run and Optional[Example] to a dictionary"""
+        if run.child_runs is None:
+            raise ValueError("Run must have child runs to be evaluated.")
+        if run.outputs is None:
+            raise ValueError("Run must have outputs to be evaluated.")
+        question = run.inputs[self.agent_input_key]
+        tool_runs = [
+            run_ for run_ in run.child_runs if run_.run_type == RunTypeEnum.tool
+        ]
+        agent_steps = []
+        for i, run_ in enumerate(tool_runs, 1):
+            tool_output = (
+                f"Tool output: {run_.outputs.get(self.tool_output_key, run_.outputs)}"
+                if run_.outputs
+                else (f"Tool error: {run_.error}" if run_.error else "No output")
+            )
+            agent_steps.append(
+                f"""Step {i}:
+Tool used: {run_.name}
+Tool input: {run_.inputs.get(self.tool_input_key, run_.inputs)}
+Tool output: {tool_output}"""
+            )
+
+        return {
+            "tool_descriptions": "\n\n".join(self.tool_descriptions),
+            "question": question,
+            "agent_trajectory": "\n\n".join(agent_steps),
+            "answer": run.outputs[self.agent_output_key],
+        }
+
+
+def get_trajectory_evaluator(
+    llm: BaseChatModel,
+    agent_tools: Union[Sequence[str], Sequence[BaseTool]],
+    *,
+    input_key: str = "input",
+    prediction_key: str = "output",
+    tool_input_key: str = "input",
+    tool_output_key: str = "output",
+    prompt: BasePromptTemplate = TRAJECTORY_PROMPT,
+    evaluation_name: str = "Agent Trajectory",
+    **kwargs: Any,
+) -> RunEvaluatorChain:
+    """Get an eval chain for grading a model's response against a map of criteria."""
+    tool_descriptions = [
+        f"Tool {i}: {tool.name}\nDescription: {tool.description}"
+        if isinstance(tool, BaseTool)
+        else f"Tool {i}: {tool}"
+        for i, tool in enumerate(agent_tools, 1)
+    ]
+
+    input_mapper = kwargs.pop(
+        "input_mapper",
+        TrajectoryInputMapper(
+            agent_input_key=input_key,
+            agent_output_key=prediction_key,
+            tool_input_key=tool_input_key,
+            tool_output_key=tool_output_key,
+            tool_descriptions=tool_descriptions,
+        ),
+    )
+    parser = kwargs.pop(
+        "output_parser",
+        TrajectoryEvalOutputParser(evaluation_name=evaluation_name),
+    )
+    eval_chain = LLMChain(llm=llm, prompt=prompt, **kwargs)
+    return RunEvaluatorChain(
+        eval_chain=eval_chain,
+        input_mapper=input_mapper,
+        output_parser=parser,
+        **kwargs,
+    )