添加记忆与对话

使用 LangChain 设计 Agentic 系统

Dilini K. Sumanapala, PhD

Founder & AI Engineer, Genverv, Ltd.

测试工具使用

添加了工具节点的完整聊天机器人图。

测试工具使用

# 生成聊天机器人图
display(Image(app.get_graph().draw_mermaid_png()))


# 定义执行聊天机器人的函数，逐条流式输出消息
def stream_tool_responses(user_input: str):
    for event in graph.stream({"messages": [("user", user_input)]}):


        # 返回代理的最后回复
        for value in event.values():
            print("Agent:", value["messages"])


# 定义查询并运行聊天机器人
user_query = "House of Lords"
stream_tool_responses(user_query)

可视化图表

带工具节点的 LangGraph 聊天机器人图

流式输出

Agent: [AIMessage(content='', additional_kwargs={'tool_calls': [{'function': {'arguments': 
'{"query":"House of Lords"}', 'name': 'wikipedia'}, 'type': 'function'}]},
response_metadata={'...'}])]

Agent: [ToolMessage(content='Page: House of Lords\nSummary: The House of Lords is the upper 
house of the Parliament of the United Kingdom. Like the lower house...The House of Lords 
also has a Church of England role...', name='wikipedia', id='...',')]

Agent: [AIMessage(content='The House of Lords is the upper house of the Parliament 
of the United Kingdom, located in the Palace of Westminster in London. It is one 
of the oldest institutions in the world..., additional_kwargs={},

response_metadata=
{'finish_reason': 'stop', 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 
'fp_0ba0d124f1'}, id='run-ae3a0b4f-5b42-4f4e-9409-7c191af0b9c9-0')]

添加记忆

# 导入用于保存记忆的模块
from langgraph.checkpoint.memory import MemorySaver


# 启用记忆检查点修改图
memory = MemorySaver()


# 编译图并传入记忆
graph = graph_builder.compile(checkpointer=memory)

基于记忆的流式输出

# 为单个用户设置流式函数
def stream_memory_responses(user_input: str):

    config = {"configurable": {"thread_id": "single_session_memory"}}


    # 流式输出图中的事件
    for event in graph.stream({"messages": [("user", user_input)]}, config):


        # 返回代理的最后回复
        for value in event.values():
            if "messages" in value and value["messages"]:
                print("Agent:", value["messages"])


stream_memory_responses("What is the Colosseum?")
stream_memory_responses("Who built it?")

使用记忆生成输出

stream_memory_responses("What is the Colosseum?")

Agent: [AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 
'id': '...', 'function': {'arguments': '
{"query":"Colosseum"}', 'name': 'wikipedia'}, ...])]

Agent: [ToolMessage(content='Page: Colosseum\nSummary: The Colosseum is an ancient
amphitheatre in Rome, Italy. It is the largest standing amphitheatre 
in the world..)]

Agent: [AIMessage(content='The Colosseum, located in Rome, is the largest ancient 
amphitheatre still standing. Built under Emperor Vespasian and completed by his son 
Titus, it hosted gladiatorial games and public events. It is also known as the Flavian 
Amphitheatre due to its association with the Flavian dynasty.', additional_kwargs={}, 
response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-4o-mini-...', ...')]

使用记忆生成输出

stream_memory_responses("Who built it?")

Agent: [AIMessage(content='The Colosseum was built by Emperor Vespasian around 
72 AD and completed by his successor, Emperor Titus. Later modifications were 
made by Emperor Domitian...')]

Passons à la pratique !

使用 LangChain 设计 Agentic 系统