您可能需要为新的运行使用不同的配置重建图。例如，您可能需要根据配置使用不同的图状态或图结构。本指南将展示如何实现这一点。
注意 在大多数情况下，根据配置自定义行为应该由一个单独的图来处理，其中每个节点都可以读取配置并根据其改变其行为。
先决条件
请务必先查阅此操作指南，了解如何设置您的应用程序以进行部署。
定义图
假设您有一个应用程序，其中包含一个简单的图，它调用一个LLM并将响应返回给用户。应用程序文件目录如下所示：
my-app/
|-- requirements.txt
|-- .env
|-- openai_agent.py # code for your graph
openai_agent.py中定义。
不重建
在标准LangGraph API配置中，服务器使用在openai_agent.py顶级定义的已编译图实例，如下所示：
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, MessageGraph
model = ChatOpenAI(temperature=0)
graph_workflow = MessageGraph()
graph_workflow.add_node("agent", model)
graph_workflow.add_edge("agent", END)
graph_workflow.add_edge(START, "agent")
agent = graph_workflow.compile()
langgraph.json）中指定包含CompiledStateGraph实例的变量路径，例如：
{
"dependencies": ["."],
"graphs": {
"openai_agent": "./openai_agent.py:agent",
},
"env": "./.env"
}
openai_agent.py，转而提供一个接受配置并返回图（或已编译图）实例的函数。假设我们希望为用户ID“1”返回现有图，并为其他用户返回一个工具调用代理。我们可以修改openai_agent.py如下：
from typing import Annotated
from typing_extensions import TypedDict
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, MessageGraph
from langgraph.graph.state import StateGraph
from langgraph.graph.message import add_messages
from langchain.tools import tool
from langgraph.prebuilt import ToolNode
from langchain.messages import BaseMessage
from langchain_core.runnables import RunnableConfig
class State(TypedDict):
messages: Annotated[list[BaseMessage], add_messages]
model = ChatOpenAI(temperature=0)
def make_default_graph():
"""Make a simple LLM agent"""
graph_workflow = StateGraph(State)
def call_model(state):
return {"messages": [model.invoke(state["messages"])]}
graph_workflow.add_node("agent", call_model)
graph_workflow.add_edge("agent", END)
graph_workflow.add_edge(START, "agent")
agent = graph_workflow.compile()
return agent
def make_alternative_graph():
"""Make a tool-calling agent"""
@tool
def add(a: float, b: float):
"""Adds two numbers."""
return a + b
tool_node = ToolNode([add])
model_with_tools = model.bind_tools([add])
def call_model(state):
return {"messages": [model_with_tools.invoke(state["messages"])]}
def should_continue(state: State):
if state["messages"][-1].tool_calls:
return "tools"
else:
return END
graph_workflow = StateGraph(State)
graph_workflow.add_node("agent", call_model)
graph_workflow.add_node("tools", tool_node)
graph_workflow.add_edge("tools", "agent")
graph_workflow.add_edge(START, "agent")
graph_workflow.add_conditional_edges("agent", should_continue)
agent = graph_workflow.compile()
return agent
# this is the graph making function that will decide which graph to
# build based on the provided config
def make_graph(config: RunnableConfig):
user_id = config.get("configurable", {}).get("user_id")
# route to different graph state / structure based on the user ID
if user_id == "1":
return make_default_graph()
else:
return make_alternative_graph()
langgraph.json中指定您的图创建函数（make_graph）的路径：
{
"dependencies": ["."],
"graphs": {
"openai_agent": "./openai_agent.py:make_graph",
},
"env": "./.env"
}
