132 lines
22 KiB
Plaintext
132 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2026-09-08T03:13:46.165453300Z",
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"start_time": "2026-09-08T03:13:45.410229600Z"
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}
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},
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"source": [
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"import logging\n",
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"from typing import TypedDict\n",
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"\n",
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"from IPython.display import display\n",
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"from langchain_core.runnables import RunnableConfig\n",
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"from langgraph.constants import START, END\n",
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"from langgraph.graph import StateGraph\n",
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"\n",
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"logger = logging.getLogger(__name__)\n",
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"\n",
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"\n",
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"# 全局状态\n",
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"class EmptyState(TypedDict):\n",
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" pass\n",
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"\n",
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"def node_a(state: EmptyState, config: RunnableConfig) -> EmptyState:\n",
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" cur_step = config[\"metadata\"][\"langgraph_step\"]\n",
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" print(f\"Node A节点当前在: {cur_step}步\")\n",
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" return {}\n",
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"\n",
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"def node_b(state: EmptyState, config: RunnableConfig) -> EmptyState:\n",
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" cur_step = config[\"metadata\"][\"langgraph_step\"]\n",
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" print(f\"Node B节点当前在: {cur_step}步\")\n",
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" return {}\n",
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"\n",
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"def node_c(state: EmptyState, config: RunnableConfig) -> EmptyState:\n",
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" cur_step = config[\"metadata\"][\"langgraph_step\"]\n",
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" print(f\"Node C节点当前在: {cur_step}步\")\n",
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" return {}\n",
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"\n",
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"def node_d(state: EmptyState, config: RunnableConfig) -> EmptyState:\n",
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" cur_step = config[\"metadata\"][\"langgraph_step\"]\n",
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" print(f\"Node D节点当前在: {cur_step}步\")\n",
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" return {}\n",
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"\n",
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"def node_e(state: EmptyState, config: RunnableConfig) -> EmptyState:\n",
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" cur_step = config[\"metadata\"][\"langgraph_step\"]\n",
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" print(f\"Node E节点当前在: {cur_step}步\")\n",
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" return {}\n",
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"\n",
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"builder = StateGraph(state_schema=EmptyState)\n",
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"\n",
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"builder.add_node(\"node_a\", node_a)\n",
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"builder.add_node(\"node_b\", node_b)\n",
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"builder.add_node(\"node_c\", node_c)\n",
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"builder.add_node(\"node_d\", node_d)\n",
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"builder.add_node(\"node_e\", node_e)\n",
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"\n",
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"builder.add_edge(START, \"node_a\")\n",
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"builder.add_edge(\"node_a\", \"node_b\")\n",
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"builder.add_edge(\"node_a\", \"node_c\")\n",
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"builder.add_edge(\"node_b\", \"node_d\")\n",
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"# 这种连接边d、c不需要都执行完也会执行e\n",
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"# builder.add_edge(\"node_d\", \"node_e\")\n",
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"# builder.add_edge(\"node_c\", \"node_e\")\n",
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"# 这种连接边d、c都要执行完才会执行e\n",
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"builder.add_edge([\"node_c\", \"node_d\"], \"node_e\")\n",
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"\n",
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"builder.add_edge(\"node_e\", END)\n",
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"\n",
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"workflow = builder.compile()\n",
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"result = workflow.invoke({})\n",
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"print(result)\n",
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"\n",
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"display(workflow)"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Node A节点当前在: 1步\n",
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"Node B节点当前在: 2步\n",
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"Node C节点当前在: 2步\n",
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"Node D节点当前在: 3步\n",
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"Node E节点当前在: 4步\n",
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"None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<langgraph.graph.state.CompiledStateGraph object at 0x000002854CCFDD10>"
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],
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 10
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|