使用时间旅行¶
在 LangGraph 中使用时间旅行
- 使用
invoke或streamAPI 通过初始输入运行图。 - 识别现有线程中的检查点：使用
get_state_history()方法检索特定thread_id的执行历史并找到所需的checkpoint_id。
或者，在您希望执行暂停的节点前设置一个断点。然后您可以找到在该断点之前记录的最新检查点。 - （可选）修改图状态：使用
update_state方法在检查点修改图的状态，并从替代状态恢复执行。 - 从检查点恢复执行：使用
invoke或streamAPI，输入为None，配置中包含相应的thread_id和checkpoint_id。
示例¶
此示例构建了一个简单的 LangGraph 工作流，用于生成笑话主题并使用 LLM 编写笑话。它演示了如何运行图、检索过去的执行检查点、可选地修改状态，以及从选定的检查点恢复执行以探索不同的结果。
设置¶
首先我们需要安装所需的包
接下来，我们需要为 Anthropic（我们将使用的 LLM）设置 API 密钥
import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")
为 LangGraph 开发设置 LangSmith
注册 LangSmith 可以快速发现问题并提高 LangGraph 项目的性能。LangSmith 允许您使用跟踪数据来调试、测试和监控您使用 LangGraph 构建的 LLM 应用程序——有关如何开始的更多信息，请参阅此处。
API 参考：StateGraph | START | END | init_chat_model | InMemorySaver
import uuid
from typing_extensions import TypedDict, NotRequired
from langgraph.graph import StateGraph, START, END
from langchain.chat_models import init_chat_model
from langgraph.checkpoint.memory import InMemorySaver
class State(TypedDict):
topic: NotRequired[str]
joke: NotRequired[str]
llm = init_chat_model(
"anthropic:claude-3-7-sonnet-latest",
temperature=0,
)
def generate_topic(state: State):
"""LLM call to generate a topic for the joke"""
msg = llm.invoke("Give me a funny topic for a joke")
return {"topic": msg.content}
def write_joke(state: State):
"""LLM call to write a joke based on the topic"""
msg = llm.invoke(f"Write a short joke about {state['topic']}")
return {"joke": msg.content}
# Build workflow
workflow = StateGraph(State)
# Add nodes
workflow.add_node("generate_topic", generate_topic)
workflow.add_node("write_joke", write_joke)
# Add edges to connect nodes
workflow.add_edge(START, "generate_topic")
workflow.add_edge("generate_topic", "write_joke")
workflow.add_edge("write_joke", END)
# Compile
checkpointer = InMemorySaver()
graph = workflow.compile(checkpointer=checkpointer)
graph
1. 运行图¶
config = {
"configurable": {
"thread_id": uuid.uuid4(),
}
}
state = graph.invoke({}, config)
print(state["topic"])
print()
print(state["joke"])
How about "The Secret Life of Socks in the Dryer"? You know, exploring the mysterious phenomenon of how socks go into the laundry as pairs but come out as singles. Where do they go? Are they starting new lives elsewhere? Is there a sock paradise we don't know about? There's a lot of comedic potential in the everyday mystery that unites us all!
# The Secret Life of Socks in the Dryer
I finally discovered where all my missing socks go after the dryer. Turns out they're not missing at all—they've just eloped with someone else's socks from the laundromat to start new lives together.
My blue argyle is now living in Bermuda with a red polka dot, posting vacation photos on Sockstagram and sending me lint as alimony.
2. 识别检查点¶
# The states are returned in reverse chronological order.
states = list(graph.get_state_history(config))
for state in states:
print(state.next)
print(state.config["configurable"]["checkpoint_id"])
print()
()
1f02ac4a-ec9f-6524-8002-8f7b0bbeed0e
('write_joke',)
1f02ac4a-ce2a-6494-8001-cb2e2d651227
('generate_topic',)
1f02ac4a-a4e0-630d-8000-b73c254ba748
('__start__',)
1f02ac4a-a4dd-665e-bfff-e6c8c44315d9
# This is the state before last (states are listed in chronological order)
selected_state = states[1]
print(selected_state.next)
print(selected_state.values)
('write_joke',)
{'topic': 'How about "The Secret Life of Socks in the Dryer"? You know, exploring the mysterious phenomenon of how socks go into the laundry as pairs but come out as singles. Where do they go? Are they starting new lives elsewhere? Is there a sock paradise we don\'t know about? There\'s a lot of comedic potential in the everyday mystery that unites us all!'}
3. 更新状态（可选）¶
update_state 将创建一个新的检查点。新检查点将与同一线程关联，但会有一个新的检查点 ID。
new_config = graph.update_state(selected_state.config, values={"topic": "chickens"})
print(new_config)
{'configurable': {'thread_id': 'c62e2e03-c27b-4cb6-8cea-ea9bfedae006', 'checkpoint_ns': '', 'checkpoint_id': '1f02ac4a-ecee-600b-8002-a1d21df32e4c'}}