an agent's trajectory (without using tracing callbacks like those in LangSmith) for evaluation is to initialize the agent with return_intermediate_steps=True.Below, create an example agent we will call to evaluate.import osimport subprocessfrom langchain.chat_models import ChatOpenAIfrom langchain.tools import toolfrom langchain.agents import AgentType, initialize_agentfrom pydantic import HttpUrlfrom urllib.parse import urlparse@tooldef ping(url: HttpUrl, return_error: bool) -> str:    """Ping the fully specified url. Must include https:// in the url."""    hostname = urlparse(str(url)).netloc    completed_process = subprocess.run(        ["ping", "-c", "1", hostname], capture_output=True, text=True    )    output = completed_process.stdout    if return_error and completed_process.returncode != 0:        return completed_process.stderr    return output@tooldef trace_route(url: HttpUrl, return_error: bool) -> str:    """Trace the route to the specified url. Must include https:// in the url."""    hostname = urlparse(str(url)).netloc    completed_process = subprocess.run(        ["traceroute", hostname], capture_output=True, text=True    )    output = completed_process.stdout    if return_error and completed_process.returncode != 0:        return completed_process.stderr    return outputllm = ChatOpenAI(model="gpt-3.5-turbo-0613", temperature=0)agent = initialize_agent(    llm=llm,    tools=[ping, trace_route],    agent=AgentType.OPENAI_MULTI_FUNCTIONS,    return_intermediate_steps=True,  # IMPORTANT!)result = agent("What's the latency like for https://langchain.com?")Evaluate Trajectory‚ÄãPass the input, trajectory, and pass to the evaluate_agent_trajectory method.evaluation_result = evaluator.evaluate_agent_trajectory(    prediction=result["output"],    input=result["input"],    agent_trajectory=result["intermediate_steps"],)evaluation_result    {'score': 1.0,     'reasoning': "i. The final answer is helpful. It directly answers the user's question about the
Open In Colab
Open In Colab ->: an agent's trajectory (without using tracing callbacks like those in LangSmith) for evaluation is to initialize the agent with return_intermediate_steps=True.Below, create an example agent we will call to evaluate.import osimport subprocessfrom langchain.chat_models import ChatOpenAIfrom langchain.tools import toolfrom langchain.agents import AgentType, initialize_agentfrom pydantic import HttpUrlfrom urllib.parse import urlparse@tooldef ping(url: HttpUrl, return_error: bool) -> str:    """Ping the fully specified url. Must include https:// in the url."""    hostname = urlparse(str(url)).netloc    completed_process = subprocess.run(        ["ping", "-c", "1", hostname], capture_output=True, text=True    )    output = completed_process.stdout    if return_error and completed_process.returncode != 0:        return completed_process.stderr    return output@tooldef trace_route(url: HttpUrl, return_error: bool) -> str:    """Trace the route to the specified url. Must include https:// in the url."""    hostname = urlparse(str(url)).netloc    completed_process = subprocess.run(        ["traceroute", hostname], capture_output=True, text=True    )    output = completed_process.stdout    if return_error and completed_process.returncode != 0:        return completed_process.stderr    return outputllm = ChatOpenAI(model="gpt-3.5-turbo-0613", temperature=0)agent = initialize_agent(    llm=llm,    tools=[ping, trace_route],    agent=AgentType.OPENAI_MULTI_FUNCTIONS,    return_intermediate_steps=True,  # IMPORTANT!)result = agent("What's the latency like for https://langchain.com?")Evaluate Trajectory‚ÄãPass the input, trajectory, and pass to the evaluate_agent_trajectory method.evaluation_result = evaluator.evaluate_agent_trajectory(    prediction=result["output"],    input=result["input"],    agent_trajectory=result["intermediate_steps"],)evaluation_result    {'score': 1.0,     'reasoning': "i. The final answer is helpful. It directly answers the user's question about the