书接上文（【AI Agent】【LangGraph】0. 快速上手：协同LangChain，LangGraph帮你用图结构轻松构建多智能体），前面我们了解了 LangGraph 的概念和基本构造方法，今天我们来看下 LangGraph 构造中的进阶用法：给边加个条件 - 条件分支（Conditional edges）。
LangGraph 构造的是个图的数据结构，有节点(node) 和边(edge），那它的边也可以是带条件的。如何给边加入条件呢？可以通过 add_conditional_edges 函数添加带条件的边。
废话不多说，先上完整代码，和运行结果。先跑起来看看效果再说。
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, BaseMessage
from langgraph.graph import END, MessageGraph
import json
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool
from langchain_core.utils.function_calling import convert_to_openai_tool
from typing import List
@tool
def multiply(first_number: int, second_number: int):
"""Multiplies two numbers together."""
return first_number * second_number
model = ChatOpenAI(temperature=0)
model_with_tools = model.bind(tools=[convert_to_openai_tool(multiply)])
graph = MessageGraph()
def invoke_model(state: List[BaseMessage]):
return model_with_tools.invoke(state)
graph.add_node("oracle", invoke_model)
def invoke_tool(state: List[BaseMessage]):
tool_calls = state[-1].additional_kwargs.get("tool_calls", [])
multiply_call = None
for tool_call in tool_calls:
if tool_call.get("function").get("name") == "multiply":
multiply_call = tool_call
if multiply_call is None:
raise Exception("No adder input found.")
res = multiply.invoke(
json.loads(multiply_call.get("function").get("arguments"))
)
return ToolMessage(
tool_call_id=multiply_call.get("id"),
content=res
)
graph.add_node("multiply", invoke_tool)
graph.add_edge("multiply", END)
graph.set_entry_point("oracle")
def router(state: List[BaseMessage]):
tool_calls = state[-1].additional_kwargs.get("tool_calls", [])
if len(tool_calls):
return "multiply"
else:
return "end"
graph.add_conditional_edges("oracle", router, {
"multiply": "multiply",
"end": END,
})
runnable = graph.compile()
response = runnable.invoke(HumanMessage("What is 123 * 456?"))
print(response)运行结果如下：
下面对上面的代码进行详细解释。
2.1 add_conditional_edges
首先，我们知道了可以通过 add_conditional_edges 来对边进行条件添加。这部分代码如下：
graph.add_conditional_edges("oracle", router, {
"multiply": "multiply",
"end": END,
})add_conditional_edges接收三个参数：
• 第一个为这条边的第一个node的名称
• 第二个为这条边的条件
• 第三个为条件返回结果的映射（根据条件结果映射到相应的node）
如上面的代码，意思就是往 “oracle” node上添加边，这个node有两条边，一条是往“multiply” node上走，一条是往“END”上走。怎么决定往哪个方向去：条件是 router（后面解释），如果 router 返回的是“multiply”，则往“multiply”方向走，如果 router 返回的是 “end”，则走“END”。
来看下这个函数的源码：
def add_conditional_edges(
self,
start_key: str,
condition: Callable[..., str],
conditional_edge_mapping: Optional[Dict[str, str]] = None,
) -> None:
if self.compiled:
logger.warning(
"Adding an edge to a graph that has already been compiled. This will "
"not be reflected in the compiled graph."
)
if start_key not in self.nodes:
raise ValueError(f"Need to add_node `{start_key}` first")
if iscoroutinefunction(condition):
raise ValueError("Condition cannot be a coroutine function")
if conditional_edge_mapping and set(
conditional_edge_mapping.values()
).difference([END]).difference(self.nodes):
raise ValueError(
f"Missing nodes which are in conditional edge mapping. Mapping "
f"contains possible destinations: "
f"{list(conditional_edge_mapping.values())}. Possible nodes are "
f"{list(self.nodes.keys())}."
)
self.branches[start_key].append(Branch(condition, conditional_edge_mapping))重点是这一句：self.branches[start_key].append(Branch(condition, conditional_edge_mapping))，给当前node添加分支Branch。
2.2 条件 router
条件代码如下：判断执行结果中是否有 tool_calls 参数，如果有，则返回"multiply"，没有，则返回“end”。
def router(state: List[BaseMessage]):
tool_calls = state[-1].additional_kwargs.get("tool_calls", [])
if len(tool_calls):
return "multiply"
else:
return "end"2.3 各node的定义
（1）起始node：oracle
@tool
def multiply(first_number: int, second_number: int):
"""Multiplies two numbers together."""
return first_number * second_number
model = ChatOpenAI(temperature=0)
model_with_tools = model.bind(tools=[convert_to_openai_tool(multiply)])
graph = MessageGraph()
def invoke_model(state: List[BaseMessage]):
return model_with_tools.invoke(state)
graph.add_node("oracle", invoke_model)这个node是一个带有Tools 的 ChatOpenAI。在LangChain中使用Tools的详细教程请看这篇文章：【AI大模型应用开发】【LangChain系列】5. LangChain入门：智能体Agents模块的实战详解。简单解释就是：这个node的执行结果，将返回是否应该使用绑定的Tools。
（2）multiply
def invoke_tool(state: List[BaseMessage]):
tool_calls = state[-1].additional_kwargs.get("tool_calls", [])
multiply_call = None
for tool_call in tool_calls:
if tool_call.get("function").get("name") == "multiply":
multiply_call = tool_call
if multiply_call is None:
raise Exception("No adder input found.")
res = multiply.invoke(
json.loads(multiply_call.get("function").get("arguments"))
)
return ToolMessage(
tool_call_id=multiply_call.get("id"),
content=res
)
graph.add_node("multiply", invoke_tool)这个node的作用就是执行Tools。
