设置¶
首先，我们需要安装所需的软件包
在 [1] 中
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%%capture --no-stderr
%pip install --quiet -U langgraph langchain_anthropic
%%capture --no-stderr %pip install --quiet -U langgraph langchain_anthropic
接下来，我们需要为 Anthropic（我们将使用的 LLM）设置 API 密钥
在 [2] 中
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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")
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")
ANTHROPIC_API_KEY: ········
在 [1] 中
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from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver
from IPython.display import Image, display
class State(TypedDict):
input: str
def step_1(state):
print("---Step 1---")
pass
def step_2(state):
print("---Step 2---")
pass
def step_3(state):
print("---Step 3---")
pass
builder = StateGraph(State)
builder.add_node("step_1", step_1)
builder.add_node("step_2", step_2)
builder.add_node("step_3", step_3)
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "step_2")
builder.add_edge("step_2", "step_3")
builder.add_edge("step_3", END)
# Set up memory
memory = MemorySaver()
# Add
graph = builder.compile(checkpointer=memory, interrupt_before=["step_3"])
# View
display(Image(graph.get_graph().draw_mermaid_png()))
from typing_extensions import TypedDict from langgraph.graph import StateGraph, START, END from langgraph.checkpoint.memory import MemorySaver from IPython.display import Image, display class State(TypedDict): input: str def step_1(state): print("---Step 1---") pass def step_2(state): print("---Step 2---") pass def step_3(state): print("---Step 3---") pass builder = StateGraph(State) builder.add_node("step_1", step_1) builder.add_node("step_2", step_2) builder.add_node("step_3", step_3) builder.add_edge(START, "step_1") builder.add_edge("step_1", "step_2") builder.add_edge("step_2", "step_3") builder.add_edge("step_3", END) # Set up memory memory = MemorySaver() # Add graph = builder.compile(checkpointer=memory, interrupt_before=["step_3"]) # View display(Image(graph.get_graph().draw_mermaid_png()))
在 [7] 中
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# Input
initial_input = {"input": "hello world"}
# Thread
thread = {"configurable": {"thread_id": "1"}}
# Run the graph until the first interruption
for event in graph.stream(initial_input, thread, stream_mode="values"):
print(event)
try:
user_approval = input("Do you want to go to Step 3? (yes/no): ")
except:
user_approval = "yes"
if user_approval.lower() == "yes":
# If approved, continue the graph execution
for event in graph.stream(None, thread, stream_mode="values"):
print(event)
else:
print("Operation cancelled by user.")
# Input initial_input = {"input": "hello world"} # Thread thread = {"configurable": {"thread_id": "1"}} # Run the graph until the first interruption for event in graph.stream(initial_input, thread, stream_mode="values"): print(event) try: user_approval = input("Do you want to go to Step 3? (yes/no): ") except: user_approval = "yes" if user_approval.lower() == "yes": # If approved, continue the graph execution for event in graph.stream(None, thread, stream_mode="values"): print(event) else: print("Operation cancelled by user.")
{'input': 'hello world'}
---Step 1---
---Step 2---
Do you want to go to Step 3? (yes/no): yes
---Step 3---
Agent¶
在 Agent 的上下文中，断点对于手动批准某些 Agent 操作很有用。
为了展示这一点，我们将构建一个相对简单的 ReAct 风格的 Agent 来进行工具调用。
我们将在调用 action 节点之前添加一个断点。
在 [2] 中
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# Set up the tool
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.graph import MessagesState, START
from langgraph.prebuilt import ToolNode
from langgraph.graph import END, StateGraph
from langgraph.checkpoint.memory import MemorySaver
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder for the actual implementation
# Don't let the LLM know this though 😊
return [
"It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
]
tools = [search]
tool_node = ToolNode(tools)
# Set up the model
model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
model = model.bind_tools(tools)
# Define nodes and conditional edges
# Define the function that determines whether to continue or not
def should_continue(state):
messages = state["messages"]
last_message = messages[-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return "end"
# Otherwise if there is, we continue
else:
return "continue"
# Define the function that calls the model
def call_model(state):
messages = state["messages"]
response = model.invoke(messages)
# We return a list, because this will get added to the existing list
return {"messages": [response]}
# Define a new graph
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Finally we pass in a mapping.
# The keys are strings, and the values are other nodes.
# END is a special node marking that the graph should finish.
# What will happen is we will call `should_continue`, and then the output of that
# will be matched against the keys in this mapping.
# Based on which one it matches, that node will then be called.
{
# If `tools`, then we call the tool node.
"continue": "action",
# Otherwise we finish.
"end": END,
},
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")
# Set up memory
memory = MemorySaver()
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
# We add in `interrupt_before=["action"]`
# This will add a breakpoint before the `action` node is called
app = workflow.compile(checkpointer=memory, interrupt_before=["action"])
display(Image(app.get_graph().draw_mermaid_png()))
# Set up the tool from langchain_anthropic import ChatAnthropic from langchain_core.tools import tool from langgraph.graph import MessagesState, START from langgraph.prebuilt import ToolNode from langgraph.graph import END, StateGraph from langgraph.checkpoint.memory import MemorySaver @tool def search(query: str): """Call to surf the web.""" # This is a placeholder for the actual implementation # Don't let the LLM know this though 😊 return [ "It's sunny in San Francisco, but you better look out if you're a Gemini 😈." ] tools = [search] tool_node = ToolNode(tools) # Set up the model model = ChatAnthropic(model="claude-3-5-sonnet-20240620") model = model.bind_tools(tools) # Define nodes and conditional edges # Define the function that determines whether to continue or not def should_continue(state): messages = state["messages"] last_message = messages[-1] # If there is no function call, then we finish if not last_message.tool_calls: return "end" # Otherwise if there is, we continue else: return "continue" # Define the function that calls the model def call_model(state): messages = state["messages"] response = model.invoke(messages) # We return a list, because this will get added to the existing list return {"messages": [response]} # Define a new graph workflow = StateGraph(MessagesState) # Define the two nodes we will cycle between workflow.add_node("agent", call_model) workflow.add_node("action", tool_node) # Set the entrypoint as `agent` # This means that this node is the first one called workflow.add_edge(START, "agent") # We now add a conditional edge workflow.add_conditional_edges( # First, we define the start node. We use `agent`. # This means these are the edges taken after the `agent` node is called. "agent", # Next, we pass in the function that will determine which node is called next. should_continue, # Finally we pass in a mapping. # The keys are strings, and the values are other nodes. # END is a special node marking that the graph should finish. # What will happen is we will call `should_continue`, and then the output of that # will be matched against the keys in this mapping. # Based on which one it matches, that node will then be called. { # If `tools`, then we call the tool node. "continue": "action", # Otherwise we finish. "end": END, }, ) # We now add a normal edge from `tools` to `agent`. # This means that after `tools` is called, `agent` node is called next. workflow.add_edge("action", "agent") # Set up memory memory = MemorySaver() # Finally, we compile it! # This compiles it into a LangChain Runnable, # meaning you can use it as you would any other runnable # We add in `interrupt_before=["action"]` # This will add a breakpoint before the `action` node is called app = workflow.compile(checkpointer=memory, interrupt_before=["action"]) display(Image(app.get_graph().draw_mermaid_png()))
在 [11] 中
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from langchain_core.messages import HumanMessage
thread = {"configurable": {"thread_id": "3"}}
inputs = [HumanMessage(content="search for the weather in sf now")]
for event in app.stream({"messages": inputs}, thread, stream_mode="values"):
event["messages"][-1].pretty_print()
from langchain_core.messages import HumanMessage thread = {"configurable": {"thread_id": "3"}} inputs = [HumanMessage(content="search for the weather in sf now")] for event in app.stream({"messages": inputs}, thread, stream_mode="values"): event["messages"][-1].pretty_print()
================================ Human Message ================================= search for the weather in sf now ================================== Ai Message ================================== [{'text': "Certainly! I'll search for the current weather in San Francisco for you. Let me use the search function to find this information.", 'type': 'text'}, {'text': None, 'type': 'tool_use', 'id': 'toolu_011ezBx5hKKjVJwqnECNPyyC', 'name': 'search', 'input': {'query': 'current weather in San Francisco'}}]
恢复
现在我们可以再次调用 Agent，没有任何输入来继续。
这将按要求运行工具。
使用输入中的 None 运行中断的图意味着“继续，就像中断没有发生一样”。
在 [6] 中
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for event in app.stream(None, thread, stream_mode="values"):
event["messages"][-1].pretty_print()
for event in app.stream(None, thread, stream_mode="values"): event["messages"][-1].pretty_print()
================================= Tool Message ================================= Name: search ["It's sunny in San Francisco, but you better look out if you're a Gemini \ud83d\ude08."] ================================== Ai Message ================================== Based on the search results, I can provide you with information about the current weather in San Francisco: The weather in San Francisco right now is sunny. It's worth noting that the search result includes a playful reference to astrology, suggesting that Geminis should "look out." However, this is likely just a humorous addition and not related to the actual weather conditions. Is there anything else you'd like to know about the weather in San Francisco or any other location?