langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain¶
class langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain[source]¶
Bases: AgentTrajectoryEvaluator, LLMEvalChain
A chain for evaluating ReAct style agents.
This chain is used to evaluate ReAct style agents by reasoning about
the sequence of actions taken and their outcomes.
Example:
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.evaluation import TrajectoryEvalChain
from langchain.tools import tool
@tool
def geography_answers(country: str, question: str) -> str:
    """Very helpful answers to geography questions."""
    return f"{country}? IDK - We may never know {question}."
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
agent = initialize_agent(
    tools=[geography_answers],
    llm=llm,
    agent=AgentType.OPENAI_FUNCTIONS,
    return_intermediate_steps=True,
)
question = "How many dwell in the largest minor region in Argentina?"
response = agent(question)
eval_chain = TrajectoryEvalChain.from_llm(
    llm=llm, agent_tools=[geography_answers], return_reasoning=True
)
result = eval_chain.evaluate_agent_trajectory(
    input=question,
    agent_trajectory=response["intermediate_steps"],
    prediction=response["output"],
    reference="Paris",
)
print(result["score"])
# 0
param agent_tools: Optional[List[langchain.tools.base.BaseTool]] = None¶
A list of tools available to the agent.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶