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聊天机器人评估作为多代理仿真

在构建聊天机器人（如客户支持助手）时，很难准确评估机器人的性能。每次代码更改后手动与其进行大量交互既耗时又繁琐。

一种使评估过程更加容易和可重复的方法是模拟用户交互。

使用LangGraph，设置起来非常简单。下面是一个创建“虚拟用户”以模拟对话的示例。

整个仿真大致如下所示：

流程图

设置

首先，让我们安装所需的包并设置API密钥

继续阅读，了解如何设置虚拟用户以模拟对话。

%%capture --no-stderr
%pip install -U langgraph langchain langchain_openai

import getpass
import os


def _set_if_undefined(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"Please provide your {var}")


_set_if_undefined("OPENAI_API_KEY")

为LangGraph开发设置LangSmith

注册LangSmith，可以快速发现并解决您的LangGraph项目中的问题，提高项目性能。LangSmith允许您使用跟踪数据来调试、测试和监控使用LangGraph构建的LLM应用程序——更多关于如何开始的信息，请参阅这里。

定义聊天机器人

接下来，我们将定义我们的聊天机器人。在这个笔记本中，我们假设该机器人的API接受一个消息列表，并以一条消息作为响应。如果您想更新此部分，只需更改本节以及下面模拟器中的“get_messages_for_agent”函数即可。

my_chat_bot中的实现是可配置的，甚至可以运行在另一个系统上（例如，如果您的系统不是在Python中运行）。

from typing import List

import openai


# This is flexible, but you can define your agent here, or call your agent API here.
def my_chat_bot(messages: List[dict]) -> dict:
    system_message = {
        "role": "system",
        "content": "You are a customer support agent for an airline.",
    }
    messages = [system_message] + messages
    completion = openai.chat.completions.create(
        messages=messages, model="gpt-3.5-turbo"
    )
    return completion.choices[0].message.model_dump()

my_chat_bot([{"role": "user", "content": "hi!"}])

{'content': 'Hello! How can I assist you today?',
 'role': 'assistant',
 'function_call': None,
 'tool_calls': None}

定义模拟用户

我们现在将定义模拟用户。 这可以是任何我们想要的东西，但我们将会将其构建为一个LangChain机器人。

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI

system_prompt_template = """You are a customer of an airline company. \
You are interacting with a user who is a customer support person. \

{instructions}

When you are finished with the conversation, respond with a single word 'FINISHED'"""

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt_template),
        MessagesPlaceholder(variable_name="messages"),
    ]
)
instructions = """Your name is Harrison. You are trying to get a refund for the trip you took to Alaska. \
You want them to give you ALL the money back. \
This trip happened 5 years ago."""

prompt = prompt.partial(name="Harrison", instructions=instructions)

model = ChatOpenAI()

simulated_user = prompt | model

API Reference: ChatPromptTemplate | MessagesPlaceholder | ChatOpenAI

from langchain_core.messages import HumanMessage

messages = [HumanMessage(content="Hi! How can I help you?")]
simulated_user.invoke({"messages": messages})

API Reference: HumanMessage

AIMessage(content='Hi, I would like to request a refund for a trip I took with your airline company to Alaska. Is it possible to get a refund for that trip?')

定义代理仿真

以下代码创建了一个LangGraph工作流以运行仿真。主要组件包括：

  1. 两个节点：一个用于模拟用户，另一个用于聊天机器人。
  2. 自身的图，带有条件停止标准。

请参阅以下代码中的注释以获取更多信息。

定义节点

首先，我们定义图中的节点。这些节点应该接收一个消息列表，并返回一个要添加到状态中的消息列表。

这些节点将是我们上面提到的聊天机器人和模拟用户的包装器。

注意： 这里有一个棘手的地方，就是如何区分消息。因为聊天机器人和我们的模拟用户都是LLM（大型语言模型），两者都会用AI消息进行回复。我们的状态将是一个交替的人类消息和AI消息的列表。这意味着对于其中一个节点，需要有一些逻辑来交换AI和人类角色。在这个例子中，我们将假设人类消息来自模拟用户。这意味着模拟用户节点中需要有一些逻辑来交换AI和人类消息。

首先，让我们定义聊天机器人节点

from langchain_community.adapters.openai import convert_message_to_dict
from langchain_core.messages import AIMessage


def chat_bot_node(state):
    messages = state["messages"]
    # Convert from LangChain format to the OpenAI format, which our chatbot function expects.
    messages = [convert_message_to_dict(m) for m in messages]
    # Call the chat bot
    chat_bot_response = my_chat_bot(messages)
    # Respond with an AI Message
    return {"messages": [AIMessage(content=chat_bot_response["content"])]}

API Reference: convert_message_to_dict | AIMessage

接下来，让我们定义模拟用户的节点。这将涉及一些逻辑来交换消息的角色。

def _swap_roles(messages):
    new_messages = []
    for m in messages:
        if isinstance(m, AIMessage):
            new_messages.append(HumanMessage(content=m.content))
        else:
            new_messages.append(AIMessage(content=m.content))
    return new_messages


def simulated_user_node(state):
    messages = state["messages"]
    # Swap roles of messages
    new_messages = _swap_roles(messages)
    # Call the simulated user
    response = simulated_user.invoke({"messages": new_messages})
    # This response is an AI message - we need to flip this to be a human message
    return {"messages": [HumanMessage(content=response.content)]}

定义边

我们现在需要定义边的逻辑。主要逻辑发生在模拟用户操作之后，并且应该导致以下两种结果之一：

  • 继续调用客户支持机器人
  • 结束对话

那么，对话结束的逻辑是什么呢？我们将定义为：人类聊天机器人回复 FINISHED（参见系统提示）或者对话超过6条消息（这是一个任意数字，只是为了保持这个示例简短）。

def should_continue(state):
    messages = state["messages"]
    if len(messages) > 6:
        return "end"
    elif messages[-1].content == "FINISHED":
        return "end"
    else:
        return "continue"

定义图

我们现在可以定义用于设置模拟的图了！

from langgraph.graph import END, StateGraph, START
from langgraph.graph.message import add_messages
from typing import Annotated
from typing_extensions import TypedDict


class State(TypedDict):
    messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)
graph_builder.add_node("user", simulated_user_node)
graph_builder.add_node("chat_bot", chat_bot_node)
# Every response from  your chat bot will automatically go to the
# simulated user
graph_builder.add_edge("chat_bot", "user")
graph_builder.add_conditional_edges(
    "user",
    should_continue,
    # If the finish criteria are met, we will stop the simulation,
    # otherwise, the virtual user's message will be sent to your chat bot
    {
        "end": END,
        "continue": "chat_bot",
    },
)
# The input will first go to your chat bot
graph_builder.add_edge(START, "chat_bot")
simulation = graph_builder.compile()

API Reference: END | StateGraph | START | add_messages

运行模拟

现在我们可以评估我们的聊天机器人了！我们可以用空消息调用它（这将模拟让聊天机器人启动初始对话）。

for chunk in simulation.stream({"messages": []}):
    # Print out all events aside from the final end chunk
    if END not in chunk:
        print(chunk)
        print("----")

{'chat_bot': AIMessage(content='How may I assist you today regarding your flight or any other concerns?')}
----
{'user': HumanMessage(content='Hi, my name is Harrison. I am reaching out to request a refund for a trip I took to Alaska with your airline company. The trip occurred about 5 years ago. I would like to receive a refund for the entire amount I paid for the trip. Can you please assist me with this?')}
----
{'chat_bot': AIMessage(content="Hello, Harrison. Thank you for reaching out to us. I understand you would like to request a refund for a trip you took to Alaska five years ago. I'm afraid that our refund policy typically has a specific timeframe within which refund requests must be made. Generally, refund requests need to be submitted within 24 to 48 hours after the booking is made, or in certain cases, within a specified cancellation period.\n\nHowever, I will do my best to assist you. Could you please provide me with some additional information? Can you recall any specific details about the booking, such as the flight dates, booking reference or confirmation number? This will help me further look into the possibility of processing a refund for you.")}
----
{'user': HumanMessage(content="Hello, thank you for your response. I apologize for not requesting the refund earlier. Unfortunately, I don't have the specific details such as the flight dates, booking reference, or confirmation number at the moment. Is there any other way we can proceed with the refund request without these specific details? I would greatly appreciate your assistance in finding a solution.")}
----
{'chat_bot': AIMessage(content="I understand the situation, Harrison. Without specific details like flight dates, booking reference, or confirmation number, it becomes challenging to locate and process the refund accurately. However, I can still try to help you.\n\nTo proceed further, could you please provide me with any additional information you might remember? This could include the approximate date of travel, the departure and arrival airports, the names of the passengers, or any other relevant details related to the booking. The more information you can provide, the better we can investigate the possibility of processing a refund for you.\n\nAdditionally, do you happen to have any documentation related to your trip, such as receipts, boarding passes, or emails from our airline? These documents could assist in verifying your trip and processing the refund request.\n\nI apologize for any inconvenience caused, and I'll do my best to assist you further based on the information you can provide.")}
----
{'user': HumanMessage(content="I apologize for the inconvenience caused. Unfortunately, I don't have any additional information or documentation related to the trip. It seems that I am unable to provide you with the necessary details to process the refund request. I understand that this may limit your ability to assist me further, but I appreciate your efforts in trying to help. Thank you for your time. \n\nFINISHED")}
----
{'chat_bot': AIMessage(content="I understand, Harrison. I apologize for any inconvenience caused, and I appreciate your understanding. If you happen to locate any additional information or documentation in the future, please don't hesitate to reach out to us again. Our team will be more than happy to assist you with your refund request or any other travel-related inquiries. Thank you for contacting us, and have a great day!")}
----
{'user': HumanMessage(content='FINISHED')}
----

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