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書接上文（【AI Agent】【LangGraph】0. 快速上手：協同LangChain，LangGraph幫你用圖結構輕松構建多智能體），前面我們了解了 LangGraph 的概念和基本構造方法，今天我們來看下 LangGraph 構造中的進階用法：給邊加個條件 - 條件分支（Conditional edges）。
LangGraph 構造的是個圖的數據結構，有節點(node) 和邊(edge），那它的邊也可以是帶條件的。如何給邊加入條件呢？可以通過 add_conditional_edges 函數添加帶條件的邊。
廢話不多說，先上完整代碼，和運行結果。先跑起來看看效果再說。
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)運行結果如下：
下面對上面的代碼進行詳細解釋。
首先，我們知道了可以通過 add_conditional_edges 來對邊進行條件添加。這部分代碼如下：
graph.add_conditional_edges("oracle", router, {
"multiply": "multiply",
"end": END,
})add_conditional_edges接收三個參數：
· 第一個為這條邊的第一個node的名稱
· 第二個為這條邊的條件
· 第三個為條件返回結果的映射（根據條件結果映射到相應的node）
如上面的代碼，意思就是往 “oracle” node上添加邊，這個node有兩條邊，一條是往“multiply” node上走，一條是往“END”上走。怎么決定往哪個方向去：條件是 router（后面解釋），如果 router 返回的是“multiply”，則往“multiply”方向走，如果 router 返回的是 “end”，則走“END”。
來看下這個函數的源碼：
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))重點是這一句：self.branches[start_key].append(Branch(condition, conditional_edge_mapping))，給當前node添加分支Branch。
條件代碼如下：判斷執行結果中是否有 tool_calls 參數，如果有，則返回"multiply"，沒有，則返回“end”。
def router(state: List[BaseMessage]):
tool_calls = state[-1].additional_kwargs.get("tool_calls", [])
if len(tool_calls):
return "multiply"
else:
return "end"（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)這個node是一個帶有Tools 的 ChatOpenAI。在LangChain中使用Tools的詳細教程請看這篇文章：【AI大模型應用開發】【LangChain系列】5. LangChain入門：智能體Agents模塊的實戰詳解。簡單解釋就是：這個node的執行結果，將返回是否應該使用綁定的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)這個node的作用就是執行Tools。
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