52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
# pip install --pre -U langchain langchain-openai
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from dotenv import load_dotenv
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import os
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load_dotenv()
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langchain_api_key = os.getenv("LANGCHAIN_API_KEY")
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from typing import TypedDict
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from langgraph.graph import StateGraph,END,START
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os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
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os.environ["LANGCHAIN_PROJECT"] = "my_demo" # 自定义项目名
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os.environ["LANGCHAIN_API_KEY"] = langchain_api_key
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# 1. 定义State(状态) -- 白板上只有一个字段"count"
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class State(TypedDict):
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count:int
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# 2. 编写Node(节点) -- 两个"工人"
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def node_a(state:State):
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# 接收当前State状态，返回要更新的部分
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print(f'[Node A]收到状态：{state}')
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return {"count":state["count"]+1}
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def node_b(state:State):
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print(f'[Node B]收到状态：{state}')
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return {"count":state["count"]+1}
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# 3. 添加Node到图中
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workflow = StateGraph(State) # 创建画布
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workflow.add_node("A",node_a) # 添加节点A
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workflow.add_node("B",node_b) # 添加节点B
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# 4. 用Edge连线
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workflow.add_edge(START,"A") # START -> A
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workflow.add_edge("A","B") # A -> B
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workflow.add_edge("B",END) # B -> END
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# 编译成可运行应用
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app = workflow.compile()
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# 传入初始状态，执行工作流
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print("---开始执行---")
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result = app.invoke({"count":1})
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print("最终状态:",result) # 输出{'count':4}
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