Custom Trajectory Evaluator

You can make your own custom trajectory evaluators by inheriting from the AgentTrajectoryEvaluator class and overwriting the _evaluate_agent_trajectory (and _aevaluate_agent_action) method.

In this example, you will make a simple trajectory evaluator that uses an LLM to determine if any actions were unnecessary.

%pip install --upgrade --quiet  langchain langchain-openai

from typing import Any, Optional, Sequence, Tuple

from langchain.chains import LLMChain
from langchain.evaluation import AgentTrajectoryEvaluator
from langchain_core.agents import AgentAction
from langchain_openai import ChatOpenAI


class StepNecessityEvaluator(AgentTrajectoryEvaluator):
    """Evaluate the perplexity of a predicted string."""

    def __init__(self) -> None:
        llm = ChatOpenAI(model="gpt-4", temperature=0.0)
        template = """Are any of the following steps unnecessary in answering {input}? Provide the verdict on a new line as a single "Y" for yes or "N" for no.

        DATA
        ------
        Steps: {trajectory}
        ------

        Verdict:"""
        self.chain = LLMChain.from_string(llm, template)

    def _evaluate_agent_trajectory(
        self,
        *,
        prediction: str,
        input: str,
        agent_trajectory: Sequence[Tuple[AgentAction, str]],
        reference: Optional[str] = None,
        **kwargs: Any,
    ) -> dict:
        vals = [
            f"{i}: Action=[{action.tool}] returned observation = [{observation}]"
            for i, (action, observation) in enumerate(agent_trajectory)
        ]
        trajectory = "\n".join(vals)
        response = self.chain.run(dict(trajectory=trajectory, input=input), **kwargs)
        decision = response.split("\n")[-1].strip()
        score = 1 if decision == "Y" else 0
        return {"score": score, "value": decision, "reasoning": response}

API Reference:

• LLMChain
• AgentTrajectoryEvaluator
• AgentAction
• ChatOpenAI

The example above will return a score of 1 if the language model predicts that any of the actions were unnecessary, and it returns a score of 0 if all of them were predicted to be necessary. It returns the string 'decision' as the 'value', and includes the rest of the generated text as 'reasoning' to let you audit the decision.

You can call this evaluator to grade the intermediate steps of your agent's trajectory.

evaluator = StepNecessityEvaluator()

evaluator.evaluate_agent_trajectory(
    prediction="The answer is pi",
    input="What is today?",
    agent_trajectory=[
        (
            AgentAction(tool="ask", tool_input="What is today?", log=""),
            "tomorrow's yesterday",
        ),
        (
            AgentAction(tool="check_tv", tool_input="Watch tv for half hour", log=""),
            "bzzz",
        ),
    ],
)

{'score': 1, 'value': 'Y', 'reasoning': 'Y'}