agents

This chain is used to evaluate ReAct style agents by reasoning about the sequence of actions taken and their outcomes. Based on the paper "ReAct: Synergizing Reasoning and Acting in Language Models" (https://arxiv.org/abs/2210.03629)

Example:

from langchain_classic.agents import AgentType, initialize_agent
from langchain_openai import ChatOpenAI
from langchain_classic.evaluation import TrajectoryEvalChain
from langchain_classic.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}."

model = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
agent = initialize_agent(
    tools=[geography_answers],
    llm=model,
    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=model, 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"])  # noqa: T201
# 0