%%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")
定义聊天机器人¶
接下来,我们将定义聊天机器人。在本笔记本中,我们假设机器人的 API 接受消息列表并以消息进行响应。如果要更新此内容,您只需更改此部分以及下面模拟器中的“get_messages_for_agent”函数。
my_chat_bot
中的实现是可配置的,甚至可以在另一个系统上运行(例如,如果您的系统未在 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。这可以是我们想要的任何东西,但我们将将其构建为 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
from langchain_core.messages import HumanMessage
messages = [HumanMessage(content="Hi! How can I help you?")]
simulated_user.invoke({"messages": messages})
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?')
定义代理模拟¶
以下代码创建了一个 LangGraph 工作流来运行模拟。主要组件是
- 两个节点:一个用于模拟用户,另一个用于聊天机器人。
- 图本身,具有条件停止条件。
阅读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。这些应该接收消息列表并返回要添加到状态的消息列表。这些将是我们在上面定义的聊天机器人和模拟用户的包装器。
注意: 此处一个棘手的问题是哪些消息是哪些。因为聊天机器人和模拟用户都是 LLM,所以它们都将以 AI 消息进行响应。我们的状态将是交替的人类和 AI 消息列表。这意味着对于其中一个节点,将需要一些逻辑来翻转 AI 和人类角色。在本例中,我们将假设 HumanMessages 是来自模拟用户的消息。这意味着我们需要在模拟用户节点中添加一些逻辑来交换 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"])]}
接下来,让我们定义模拟用户节点。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之后,它应该导致以下两种结果之一
- 要么我们继续并调用客户支持机器人
- 要么我们结束对话。
那么,对话结束的逻辑是什么?我们将将其定义为人类聊天机器人以“FINISHED”进行回复(请参阅系统提示)或对话超过 6 条消息(这是一个任意数字,只是为了让此示例保持简短)。
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()
运行模拟¶
现在我们可以评估聊天机器人了!我们可以使用空消息调用它(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')} ----