langgraph-learning/chapter02/03_fan_in.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2026-08-01T05:23:05.461288Z",
     "start_time": "2026-08-01T05:23:05.449204Z"
    }
   },
   "source": [
    "\"\"\"\n",
    "START --> (router) --> mapper_node --> (reducer字段合并) --> reducer_node\n",
    "                   --> mapper_node -->\n",
    "\"\"\"\n",
    "\n",
    "from typing import TypedDict, Annotated, Sequence\n",
    "\n",
    "from operator import add\n",
    "\n",
    "\n",
    "class OverallState(TypedDict):\n",
    "    input_values: list[str]\n",
    "    entries: Annotated[list[tuple[str, int]], add]\n",
    "    word_counts: dict[str, int]\n",
    "\n",
    "\n",
    "class MaperInputState(TypedDict):\n",
    "    input_value: str"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-08-01T05:23:05.475801Z",
     "start_time": "2026-08-01T05:23:05.462674Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from langgraph.types import Send\n",
    "\n",
    "\n",
    "def router_node(state: OverallState) -> Sequence[Send]:\n",
    "    task = []\n",
    "    for input_value in state[\"input_values\"]:\n",
    "        task.append(\n",
    "            Send(\"mapper_node\", arg={\"input_value\": input_value})\n",
    "        )\n",
    "    return task\n",
    "\n",
    "\n",
    "def mapper_node(state: MaperInputState) -> OverallState:\n",
    "    words = state[\"input_value\"].split(\" \")\n",
    "    entries = []\n",
    "    for word in words:\n",
    "        entries.append((word, 1))\n",
    "    return {\n",
    "        \"entries\": entries,\n",
    "    }\n",
    "\n",
    "\n",
    "def reducer_node(state: OverallState) -> OverallState:\n",
    "    entries = state[\"entries\"]\n",
    "    shuffle_dict = {}\n",
    "    for k, v in entries:\n",
    "        if k not in shuffle_dict:\n",
    "            shuffle_dict[k] = v\n",
    "        else:\n",
    "            shuffle_dict[k] += v\n",
    "    return {\n",
    "        \"word_counts\": shuffle_dict\n",
    "    }"
   ],
   "id": "acfc45665818b2c3",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-08-01T05:23:05.486771Z",
     "start_time": "2026-08-01T05:23:05.479810Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from langgraph.graph import StateGraph, START, END\n",
    "\n",
    "builder = StateGraph(state_schema=OverallState)\n",
    "# builder.add_node(\"router_ndoe\")\n",
    "builder.add_node(\"mapper_node\",mapper_node)\n",
    "builder.add_node(\"reducer_node\",reducer_node)\n",
    "\n",
    "builder.add_conditional_edges(START, router_node, path_map=[\"mapper_node\"])\n",
    "builder.add_edge(\"mapper_node\", \"reducer_node\")\n",
    "builder.add_edge(\"reducer_node\", END)\n",
    "\n",
    "graph = builder.compile()"
   ],
   "id": "a58b2eee06957fa8",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-08-01T05:23:09.162442Z",
     "start_time": "2026-08-01T05:23:05.490834Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from IPython.display import display\n",
    "display(graph)"
   ],
   "id": "f9330e28be72d83d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<langgraph.graph.state.CompiledStateGraph object at 0x10ebdcf50>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-08-01T05:23:09.188521Z",
     "start_time": "2026-08-01T05:23:09.179066Z"
    }
   },
   "cell_type": "code",
   "source": "res=graph.invoke({\"input_values\": [\"hello world1\", \"hello world2\",\"hello world3\"]})",
   "id": "884a4663016ef510",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-08-01T05:23:09.254264Z",
     "start_time": "2026-08-01T05:23:09.192955Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from rich import print as rp\n",
    "\n",
    "rp(res)"
   ],
   "id": "8426d824fcc37841",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001B[1m{\u001B[0m\n",
       "    \u001B[32m'input_values'\u001B[0m: \u001B[1m[\u001B[0m\u001B[32m'hello world1'\u001B[0m, \u001B[32m'hello world2'\u001B[0m, \u001B[32m'hello world3'\u001B[0m\u001B[1m]\u001B[0m,\n",
       "    \u001B[32m'entries'\u001B[0m: \u001B[1m[\u001B[0m\u001B[1m(\u001B[0m\u001B[32m'hello'\u001B[0m, \u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1m(\u001B[0m\u001B[32m'world1'\u001B[0m, \u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1m(\u001B[0m\u001B[32m'hello'\u001B[0m, \u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1m(\u001B[0m\u001B[32m'world2'\u001B[0m, \u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1m(\u001B[0m\u001B[32m'hello'\u001B[0m, \u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m, \u001B[1m(\u001B[0m\u001B[32m'world3'\u001B[0m, \u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m\u001B[1m]\u001B[0m,\n",
       "    \u001B[32m'word_counts'\u001B[0m: \u001B[1m{\u001B[0m\u001B[32m'hello'\u001B[0m: \u001B[1;36m3\u001B[0m, \u001B[32m'world1'\u001B[0m: \u001B[1;36m1\u001B[0m, \u001B[32m'world2'\u001B[0m: \u001B[1;36m1\u001B[0m, \u001B[32m'world3'\u001B[0m: \u001B[1;36m1\u001B[0m\u001B[1m}\u001B[0m\n",
       "\u001B[1m}\u001B[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'input_values'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'hello world1'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hello world2'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hello world3'</span><span style=\"font-weight: bold\">]</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'entries'</span>: <span style=\"font-weight: bold\">[(</span><span style=\"color: #008000; text-decoration-color: #008000\">'hello'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'world1'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'hello'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'world2'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'hello'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'world3'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)]</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'word_counts'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'hello'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'world1'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'world2'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'world3'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">}</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  }
 ],
 "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",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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